Recent Articles

Nasheeda T M  ·  International Journal of Technology and Emerging Research  ·  23 Aug 2026

Artificial intelligence (AI) is becoming an important technology in modern healthcare because of its ability to analyze large volumes of clinical, biomedical, and patient-generated data. AI-based systems are being applied in disease diagnosis, medical imaging, drug discovery, personalized medicine, clinical decision support, remote monitoring, and healthcare administration. This review examines recent trends in the use of AI in healthcare by analyzing six influential studies published between 2017 and 2024. The reviewed literature indicates that machine learning, deep learning, explainable AI, and human–AI collaboration can improve diagnostic accuracy, efficiency, and patient-centered care. However, challenges related to data privacy, algorithmic bias, explainability, cybersecurity, clinical validation, regulation, and unequal access continue to restrict large-scale implementation. The review concludes that AI should be implemented as a supportive tool under appropriate clinical supervision, ethical standards, and regulatory frameworks.

Hanna Rasheed, Arya.K.R, Ashida.K.A  ·  International Journal of Technology and Emerging Research  ·  23 Aug 2026

Cardiovascular disease (CVD) is still the number one cause of death worldwide and many patients are exposed to severe cardiac events only after the disease has progressed to an advanced stage. Recent advances in artificial intelligence (AI) and machine learning (ML) have shown great potential in improving the early prediction of cardiovascular risk from electronic health records, physiological measurements and other clinical data. This paper provides an analytical review of recent studies on ML-based approaches for early heart disease and cardiogenic shock prediction. The study evaluates the performance of popular algorithms including Logistic Regression, Support Vector Machines, Random Forests, Gradient Boosting Machines, and neural networks. It also investigates the effect of data preparation techniques such as feature scaling, normalisation, and class balancing on prediction outcomes.Results show that ML models are superior to traditional risk score methods in terms of accuracy and can detect high risk patients much earlier than traditional clinical practice. However, data heterogeneity, missing data, model interpretability, and limited clinical validation continue to pose challenges for broad implementation, despite these promising results. The study concludes that there is an urgent need for explainable, clinically validated and standardised ML frameworks to translate predictive models into routine healthcare practice and improve early detection of cardiovascular disease.

Catherine. N. B.  ·  International Journal of Technology and Emerging Research  ·  23 Aug 2026

Artificial Intelligence in Education Focused on Standardized Learning: A Learning Analytics Review Artificial Intelligence (AI) has emerged as a transformative force in education, reshaping teaching, learning, assessment, and educational management. Recent research highlights the growing integration of AI technologies such as machine learning, deep learning, natural language processing, learning analytics, recommendation systems, and generative AI across diverse educational contexts. While standardized learning seeks to ensure consistency in curriculum delivery and learning outcomes, AI-driven educational systems provide opportunities to enhance learner engagement, academic performance, and instructional effectiveness through data-driven insights. This analytical review synthesizes findings from recent studies on AI applications in education, including personalized learning, learning analytics, self-regulated learning, agentic AI, inclusive education, recommendation systems, AI literacy, and K–12 educational environments. The review examines key analytical indicators such as student achievement, engagement, learning behavior, retention, assessment performance, and adaptive learning outcomes. Findings indicate that AI-powered learning analytics can support standardized learning by enabling continuous monitoring, predictive modeling, personalized feedback, and evidence-based decision-making while maintaining common educational standards. The analysis further reveals that AI technologies contribute to improved accessibility, inclusiveness, and learning efficiency. However, challenges related to ethical concerns, data privacy, algorithmic bias, transparency, and digital equity remain significant barriers to implementation. The study concludes that the integration of AI and learning analytics has the potential to strengthen standardized learning systems by balancing educational consistency with learner-centered support, thereby improving overall educational quality and outcomes.

Sivanjali.V, Aryananda Ms, Abhirami Anilkumar, Nimisha Prakash  ·  International Journal of Technology and Emerging Research  ·  23 Aug 2026

Mental health conditions are one of the most pressing public health issues globally, impacting people of all ages and making it difficult for people to operate in health care systems around the world. As Generative Artificial Intelligence (GenAI) evolves quickly, it offers novel solutions for mental health care, like AI-powered chatbots, virtual assistants, emotion recognition systems, and personalized digital support platforms. This paper is an analysis of recent studies on the use of generative AI in mental health care which will compare the advantages, drawbacks, and ethical considerations of generative AI. The studies reviewed suggest that AI tools can help with greater accessibility of mental health supports, offer around-the-clock support, increase self-awareness, and decrease loneliness through personalized interactions. However, there are a number of issues to contend with: emotional reliance on AI companions, misinformation, privacy and security concerns, algorithmic bias, and a lack of clinical validation. This paper highlights insights from various studies on the current impact of Generative AI on mental health care and pinpoints areas where research is currently missing. The analysis recommends that Generative AI can revolutionize mental care but can't be a direct substitute for mental health professionals. Moving forward, it is crucial to promote responsible implementation of AI, ethical guidelines, transparency, and robust regulations to maintain safe and reliable mental health care services.

Jisna C Jeejo, Habeeba M A  ·  International Journal of Technology and Emerging Research  ·  23 Aug 2026

Neuromorphic computing borrows its design logic from the nervous system rather than from the von Neumann architecture that has dominated computing for seventy years. Instead of shuttling data back and forth between separate memory and processing units, it favors event-driven communication, computation that happens close to (or inside) memory, and massive parallelism across simple processing elements. This paper takes stock of where the field currently stands: spiking neural networks, digital and analog processors, memristive and other emerging devices, the software ecosystems that support them, and application areas ranging from robotics and edge intelligence to biomedical monitoring and event-based vision. What emerges from the recent literature is a field that has largely moved past small proof-of-concept chips and is now building larger, more programmable platforms with tighter hardware-algorithm integration. Even so, real obstacles remain around training methods, benchmarking practices, programmability, device variability, and fabrication, and it is still unclear how much of the field's energy advantage survives contact with general-purpose workloads. The view taken here is that brain-inspired computing is heading toward a hybrid future: conventional digital processors will keep doing what they do best, while event-driven and in-memory accelerators take over the workloads where they have a genuine edge. Neuromorphic computing is therefore unlikely to displace mainstream AI hardware outright, but it looks well positioned to become a key ingredient in low-power, adaptive, real-time intelligence at the edge.

Lovely Jacob Aloor, Sneha Joshy, Moly PP, Jesy E J, Nimisha M P  ·  International Journal of Technology and Emerging Research  ·  23 Aug 2026

Natural products are a prominent source of structurally diverse bioactive molecules for anticancer drug discovery. Among these, anthraquinones are a class of compounds with antioxidant, anti-inflammatory, antimicrobial, and anticancer activities. Damnacanthal is a naturally occurring anthraquinone from Morinda citrifolia L., which has been reported to possess diverse biological activities; however, its detailed experimental characterisation and in silico analysis are lacking. The current study aimed to isolate Damnacanthal from M. citrifolia and evaluate its antioxidant activity. In addition, the cytotoxic potential of the compound was assessed against Dalton's lymphoma ascites (DLA) cells. The drug-likeness and pharmacokinetic properties of the compound were predicted in silico. The compound isolated from M. citrifolia was identified as Damnacanthal through UV-Vis, FTIR, 1H, and 13C NMR, and LC-HRMS spectroscopy. Damnacanthal showed significant antioxidant activity through the FRAP assay. The compound exhibited cytotoxic potential against Dalton's lymphoma ascites cells in a concentration-dependent manner. Furthermore, the compound showed excellent physicochemical properties, oral drug likeness, and promising pharmacokinetic properties. The present study demonstrated that Damnacanthal is a potential natural anticancer candidate for further drug discovery and development. This study also highlighted the recent trends in AI-driven drug discovery for anticancer drug development, including QSAR, ADMET prediction, molecular docking, molecular dynamics simulation, XAI, and generative AI for de novo design. Although AI-driven approaches were not used in the present study, the results provide an AI-ready dataset to explore the anticancer potential of Damnacanthal and other anthraquinone derivatives for anticancer drug discovery and development.

Jestin James M  ·  International Journal of Technology and Emerging Research  ·  23 Aug 2026

Artificial Intelligence (AI) is changing higher education through individualized learning, intelligent tutoring systems, learning analytics, automated assessment and generative AI applications. AI is being adopted by universities around the world to improve the quality of teaching, learning, research, and institutional administration [1], [2]. AI-based systems increase student engagement with adaptive learning, provide immediate feedback, and help educators create instructional content. Generative AI tools have also boosted digital transformation by helping academic writing, programming and knowledge discovery [3], [4]. However, the adoption of AI is still hampered by issues including data privacy, algorithmic bias, academic integrity and ethical governance [5]. This review summarizes recent advances in AI for higher education and discusses major applications, benefits, obstacles, and prospects for the future with a special focus on the Indian higher education context. The review concludes that AI should complement educators rather than replace them and that responsible implementation supported by institutional policies, faculty development, and ethical guidelines is essential for sustainable educational transformation.

Thasleena T B, Shirin Shahnas B V, Muhsina V J, Suhaila T  ·  International Journal of Technology and Emerging Research  ·  23 Aug 2026

As financial institutions transition from traditional statistical models to complex "black-box" AI systems, the demand for transparency has created a critical interpretability gap. This paper provides a comparative review of the regulatory trends and technical implementations of Explainable AI (XAI) in developed (EU/US) versus emerging (India/Vietnam) economies. While developed markets rely on prescriptive mandates like the GDPR and EU AI Act, emerging economies are navigating a spectrum of governance models. Specifically, India maintains an adaptive, principles-based approach through frameworks like the NITI Aayog’s Responsible AI guidelines and RBI’s Digital Lending Guidelines, which prioritise flexibility and sectoral oversight rather than a standalone AI law. In contrast, Vietnam is transitioning toward strict legal boundaries with its Law on Artificial Intelligence (2026), which classifies banking as high-risk. Through a qualitative thematic synthesis, the study identifies that emerging markets often face unique technical and resource hurdles, leading to the adoption of hybrid and heuristic XAI models. The results suggest that XAI in finance is both a technical and a governance challenge, requiring a balance between model performance and the need for trustworthy AI governance. The paper concludes that bridging the digital divide in XAI tools is essential for ensuring financial inclusion and long-term stability in global financial systems.

Akshara B Krishnan, Anjana M R, Jeslin C J  ·  International Journal of Technology and Emerging Research  ·  23 Aug 2026

This study explores the role of Artificial Intelligence (AI) in smart traffic monitoring and road accident prevention. The main objective is to understand how AI can improve road safety by monitoring traffic, reducing congestion, and preventing accidents. The study discusses the importance of AI-based traffic management systems, common causes of road accidents, and the challenges involved in implementing these technologies. Information was collected from research articles, government reports, and other reliable sources. The findings show that AI technologies, such as smart traffic signals, real-time monitoring, and data analysis, can improve traffic flow and help reduce accidents. The study also highlights the need for public awareness, proper infrastructure, and government support for the successful implementation of AI-based traffic systems. In conclusion, Artificial Intelligence has the potential to make transportation safer and more efficient by supporting better traffic management and reducing road accidents.

Nisha c D, Dr.Thomas Monoth  ·  International Journal of Technology and Emerging Research  ·  23 Aug 2026

Image steganography performance depends on a trade-off among embedding capacity, imperceptibility, structural fidelity, robustness, security, and computational cost. This paper presents a comparative evaluation framework for spatial-domain and transform-domain image steganography using four standard benchmark images: Lena, Baboon, Barbara, and Cameraman. Representative spatial methods, Least Significant Bit (LSB) substitution and Pixel Value Differencing (PVD), are compared with transform-domain methods based on the Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and a hybrid DWT–DCT approach. The evaluation parameters include embedding capacity, bits per pixel, PSNR, SSIM, extraction accuracy, robustness, and execution complexity. Equations, image-wise tables, average-performance tables, and bar charts are provided. The numerical comparison tables are explicitly presented as an illustrative experimental template because measured values require implementation under identical payload, image size, and attack conditions.

Anaswara K.S, Krishna Mukundan M  ·  International Journal of Technology and Emerging Research  ·  23 Aug 2026

We are currently facing a growing challenge known as the "hidden cost of intelligence," driven by the immense energy consumption required to develop and practically deploy artificial intelligence (AI) models. This research paper analyzes the environmental impact of AI models and evaluates trends in their energy efficiency by integrating data from existing research and benchmark studies. The analysis shows that inference is the main source of energy consumption, accounting for about 90% of an AI model's total lifetime energy use. It also highlights a major measurement challenge, as energy consumption estimates can differ by up to 2.4 times depending on how the system boundaries are defined. Transparency is still a major issue, as more than 84% of AI models released since 2022 do not provide information about their environmental impact. However, methods like 4-bit quantization show the potential of Green AI by reducing emissions by up to 55% without affecting model accuracy. This study concludes that future AI research should give equal importance to energy efficiency and performance instead of focusing only on improving accuracy. Adopting Green AI practices, along with standardized methods for measuring and reporting energy consumption, will improve transparency, increase accountability, and support the long-term sustainability of AI technologies.

Sneha.V.K  ·  International Journal of Technology and Emerging Research  ·  23 Aug 2026

The emergence of Generative Artificial Intelligence (GenAI) has stimulated a significant transformation in higher education, aligning with United Nations Sustainable Development Goals while simultaneously presenting a "sustainability trade-off." While GenAI offers emerging opportunities for individualized learning, enhanced accessibility, and the development of transferable skills such as critical thinking and creativity. The sustainable integration of GenAI remains challenging because the training and deployment of large language models require energy-intensive computational infrastructure, leading to increased carbon emission and water consumption, while simultaneously introducing ethical challenges such as academic integrity, transparency and algorithmic bias. This paper presents a Systematic Literature Review (SLR) conducted in accordance with the PRISMA 2020 guidelines, synthesizing findings from 32 recent scholarly works published between 2022 and 2026. The review employs the Population–Exposure–Outcome (PEO) framework to examine how GenAI restructures learning environments across dimensions of operational efficiency, pedagogy, and ideology. Key results identify five strategic processes for sustainable implementation: ethical appropriation, infrastructure management, faculty development, curricular transformation, and pedagogical innovation. Furthermore, the review addresses global power dynamics, highlighting a shift toward plurality while cautioning against algorithmic colonialism. We propose the Generative AI-Enabled Sustainable Education (GAISE) framework as a roadmap for institutional resilience. The study concludes that the long-term sustainability of GenAI in education depends on balancing technological innovation with environmental transparency and ethical stewardship, advocating for longitudinal research to monitor future cognitive and ecological impacts

RIYA JACOB K  ·  International Journal of Technology and Emerging Research  ·  23 Aug 2026

Artificial intelligence has made a great impact on healthcare by providing accurate disease diagnosis, personalised treatment regimens, and efficient clinical decision making. But many of the advanced machine learning and deep learning models are black-box systems, and healthcare professionals find it difficult to understand the logic behind their predictions. This opacity hinders the adoption of intelligent systems in clinical settings where trust and accountability are a must. In this review paper we compare the main interpretability techniques that have been used in clinical decision support systems. These techniques include Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), saliency maps, Gradient-weighted Class Activation Mapping (Grad-CAM), attention mechanisms, and decision trees, among others. We performed a systematic literature review to evaluate these techniques based on interpretability, computational complexity, scalability, transparency, and clinical relevance. A systematic literature review was performed to evaluate the techniques in terms of interpretability, computational complexity, scalability, transparency and clinical relevance. The analysis shows that SHAP provides complete local and global explanations, while LIME provides computationally efficient local interpretations. Visualisation based methods such as Grad-CAM and saliency maps are especially useful for medical image analysis, while attention mechanisms are suitable for sequential healthcare data. The study concludes that explainable artificial intelligence improves trust, reliability, and accountability in healthcare systems and is a prerequisite for successful integration of intelligent technologies into clinical practice.

MS. MANJUSHA K M  ·  International Journal of Technology and Emerging Research  ·  23 Aug 2026

Psychological stress among students is a critical public health issue, it affects academic performance and long-term mental stability. Traditional diagnostic methods like interviews and periodic counselling, which failed to provide continuous and personalized monitoring. This proposal introduces an automated framework Internet of Things (IoT) wearables and Artificial Intelligence (AI) to monitor physiological stress- to detect student stress in real time. The framework employs machine learning and deep learning models for multimodal stress prediction. It recommends personalized yoga interventions based on individual stress profiles. To address the "black box" nature of current deep learning architectures, we incorporate Explainable AI (XAI) techniques like SHAP and LIME. These used to ensure clinical transparency and user trust. This enables students and healthcare professionals to understand the factors influencing stress. The system is expected to enhance early stress detection, improve effectiveness, support decision making by healthcare professionals, and also encourage student engagement in preventive wellness practice. Performance may be evaluated using Accuracy, Precision, Recall, F1-score, Mean Absolute Error and Explainability Score. In addition, the system sets off a personalized yoga recommendation engine that maps detected autonomic states to evidence-based asanas from the Common Yoga Protocol (CYP) to effectively restore emotional wellness. The proposed system aims to resolve the stress and provide personalized behavioral recovery to enhance student well-being.

Vaishnavy KU, Sajitha Sana P, Devika Pradhan P, Nandhana Pradeep, Niranjana CS  ·  International Journal of Technology and Emerging Research  ·  23 Aug 2026

Artificial general intelligence (AGI) describes a form of artificial intelligence capable of handling diverse intellectual tasks with human-like adaptability and proficiency. Unlike narrow AI, which is built for specific functions, AGI stands out due to its ability to exercise sound judgment, adapt across domains, and manage a wide variety of challenges. As AI becomes more embedded in everyday life, achieving AGI represents a pivotal advancement in technology. Yet, significant theoretical and technical obstacles remain. This paper reviews existing literature to outline the primary hurdles in AGI research, including limited common sense reasoning, struggles with rare or unexpected scenarios, issues with generating inaccurate information, constrained memory, and weaknesses in reasoning, planning, and lifelong learning. The analysis suggests that progress may come from hybrid models, embodied cognitive approaches, and improved reasoning mechanisms. Improving generalization, computational scalability, robustness, explainability, verification, and safety will also be essential for developing reliable and adaptable AGI systems. Overall, the study presents a detailed examination of current technical barriers and helps shape future research directions in AGI development.

Neha Nesnin, Nedha Haris, Jyothika V.V, Labeeba Shaju, Farhana N. A  ·  International Journal of Technology and Emerging Research  ·  23 Aug 2026

The rapid integration of Artificial Intelligence (AI) into education has transformed the way students learn, solve problems, and access information. AI-powered tools such as ChatGPT, Gemini, Microsoft Copilot, and intelligent tutoring systems have emerged as valuable learning companions, providing instant explanations, personalized guidance, and academic support. However, the increasing dependence on these technologies has raised concerns about cognitive offloading, a phenomenon in which individuals rely on external tools to perform cognitive tasks that would otherwise require memory, reasoning, or critical thinking. This paper provides an analytical review of recent research to examine whether AI is causing students to think less or enabling them to learn differently. By analyzing existing studies, the paper explores the impact of AI-assisted learning on critical thinking, memory retention, problem-solving abilities, creativity, academic performance, and learner autonomy. The findings indicate that while AI can enhance personalized learning, improve accessibility, and reduce cognitive workload, excessive dependence may weaken deep learning, independent reasoning, and long-term knowledge retention. The study concludes that AI should be viewed as a supportive educational partner rather than a replacement for human thinking. A balanced approach that combines AI-assisted learning with active cognitive engagement is essential to ensure meaningful learning and the development of higher-order thinking skills in the age of artificial intelligence.

Kavya M M, Anakha P P, Anagha V S, Krishna Madhu, Niranjana A P  ·  International Journal of Technology and Emerging Research  ·  23 Aug 2026

Per- and polyfluoroalkyl substances (PFAS), which are extremely persistent in the environment and bioaccumulate in humans and animals, have become a serious threat to the environment and human health. Traditional analytical and biochemical techniques, such as chromatographic and mass spectrometric methods, are characterized by high accuracy but are associated with high costs and low feasibility for continuous real-time monitoring. Recent advances in artificial intelligence (AI), machine learning (ML), internet of things (IoT) technologies, smart and biosensors enable novel approaches for the rapid and economic detection of PFAS and assessment of water quality. The current review focuses on recent advances in AI-assisted PFAS detection and monitoring through the use of intelligent sensors, IoT-based water monitoring systems, optical and electrochemical biosensors, and machine learning algorithms. The potential of the discussed approaches for the prediction of PFAS sources and contamination fates, as well as the implementation of smart water management systems for sustainable development, are evaluated. Particular attention is paid to the critical challenges associated with the creation of novel PFAS monitoring concepts, including the availability of high-quality data sets, sensor validation and calibration, issues of cybersecurity and data privacy, and the feasibility of implementing AI-driven approaches in practice. The research directions related to explainable AI, edge intelligence, and digital twins, which can be employed for developing autonomous monitoring systems for smart water management, are highlighted. Overall, the present review aims to provide an insight into the intelligent technologies that support the needs of PFAS recognition, monitoring, and management and promote sustainable development.

BINJI SEIDU ZAKARIA  ·  International Journal of Arts, Culture and Creative Studies  ·  23 Aug 2026

This paper examined the potency and efficacy of TfD in promoting positive attitudes to sanitation practices in a rural community from a student practitioner perspective. The facilitator of this study employed the TfD methodology or intervention to address the sanitation challenges together with the community, which is viewed as action research. Poor sanitation has far-reaching effects across all aspects of human development, be it education, health, economic, or social. Poor hygiene and sanitation practices predispose the population to avoidable diseases, which exact a heavy toll on productivity. The increased volume of household waste generation has been a general concern for the Effutu Municipality waste management and disposal system, where most of the household accumulated waste ends up not being properly disposal off in most communities, resulting in health hazards, flooding, environmental degradation, and breeding sites for mosquitoes. Household waste is not properly disposed of in the Warabeba fishing community, leading to unsightly refuse dumps scattered all over the community. Such practices lead to poor sanitation coupled with numerous diseases. Poor sanitation could therefore thwart the government's efforts to fight poverty because most people would fall under that problem, and increase the medical bill for treatment. The effects of these practices on the Warabeba community members have not received the needed attention from TfD practitioners and researchers. This study therefore seeks to bridge these gaps in knowledge.

Afna Ashraff M, Archana K, Buthaina, Krishnaja Radhakrishnan, Lakshmi K R  ·  International Journal of Technology and Emerging Research  ·  22 Aug 2026

Machine learning models, and deep neural networks in particular, now inform decisions in healthcare, finance, criminal justice, and other high-stakes domains, yet their internal reasoning remains largely opaque to the people who rely on their outputs. Explainable Artificial Intelligence (XAI) is the body of methods and design principles that render such black-box systems interpretable to developers, regulators, and end users without materially degrading predictive performance. This review surveys the XAI landscape along four dimensions: the major families of explanation techniques-intrinsically interpretable models, post-hoc local methods such as LIME and SHAP, gradient- and attention-based visual explanations such as Grad-CAM, and counterfactual explanations; their realworld applications in domains including healthcare diagnostics, credit and financial risk scoring, and autonomous and safety-critical systems; the challenges that continue to limit adoption, including explanation fidelity, the absence of standardised evaluation metrics, computational cost, and scalability to large generative models; and the future directions the field must pursue. Drawing on a structured review of the recent literature, we compare techniques along scope, fidelity, and cost, and examine the regulatory pressures, including the EU AI Act and the GDPR "right to explanation," that are accelerating adoption. The synthesis shows that no single XAI technique is universally superior; effective explainability requires matching the method to the model class, the application domain, and the stakes of the decision. We conclude that explainability is a necessary, though not sufficient, condition for trustworthy Al, and outline concrete directions for future research, including standardised benchmarks, human-centred evaluation, and explainability for large generative models.

Lakshmi C Dileesh, MV Vrendha, Fidha Fathima N, Nimra Nazrin, Shifa Ummer  ·  International Journal of Technology and Emerging Research  ·  22 Aug 2026

Abstract: Artificial Intelligence (AI) has emerged as a transformative technology that is reshaping the e-commerce industry by enhancing customer experiences, improving business efficiency, and enabling data-driven decision-making. This paper presents a comprehensive study of AI applications in modern e-commerce, including personalized product recommendations, intelligent chatbots, demand forecasting, fraud detection, dynamic pricing, inventory management, and customer sentiment analysis. It examines how AI technologies such as Machine Learning, Natural Language Processing (NLP), Computer Vision, and Predictive Analytics help businesses understand customer behavior, automate operations, optimize marketing strategies, increase sales, and reduce operational costs. The study also discusses key challenges associated with AI adoption, including data privacy, algorithmic bias, cybersecurity risks, implementation costs, and ethical concerns. The research is based on a review of recent academic literature, industry reports, and case studies from leading e-commerce platforms. The findings indicate that AI has become a major driver of innovation and competitiveness by enabling personalized, secure, and efficient shopping experiences. Furthermore, the paper highlights emerging trends such as Generative AI, conversational commerce, AI-powered virtual shopping assistants, voice commerce, and autonomous retail systems that are expected to shape the future of e-commerce. The study concludes that the responsible integration of AI technologies will continue to revolutionize the e-commerce sector while addressing technical, ethical, and regulatory challenges.