The recent and rapid rise of Artificial Intelligence (AI) and Machine Learning (ML) is affecting the sphere of auditing and financial reporting due to its potential to contribute to introducing a better accuracy level, effectiveness, and level of analysis. This study analyses the possibilities and threats of applying AI and ML in the auditing procedure and the financial reporting procedures. Applying natural language processing model and explainable artificial intelligence model, with the help of supervised and unsupervised machine learning methods, auditors can reveal anomalies, predict future wrong statements in a financial report and speed up decision-making activities. The research proposes the implementation of hybrid methodology of AI-led information and human professional judgment to achieve the integrity of the audit and ethical and regulatory consistency. Findings indicate that the employment of AI and ML can enhance efficiencies and automate repetitive operations, monitor conditions and keep checking against risks, but also enhance the quality of financial reporting by minimizing errors and maximizing the ease at which complicated datasets may be efficiently assessed. Nevertheless, the paper also highlights certain critical issues, e.g., the bias in algorithms, the drawbacks of the quality of data, cybersecurity threats, and even the severance of overreliance on automated products. The study points to the relevance of the governance framework, the ethical principles, and human supervision in maximizing the advantages of AI-based auditing and minimizing the risks related to the latter. The work has a contribution to the literature as it offers an integrated framework of embedding AI and ML in the audit and reporting activities that balance its performance in terms of efficiency, transparency and accountability.