Artificial Intelligence (AI) is transforming pharmacovigilance by enhancing the detection, assessment, and prevention of adverse drug reactions (ADRs). Leveraging technologies such as Machine Learning (ML), Natural Language Processing (NLP), and Robotic Process Automation (RPA), AI enables the analysis of vast datasets from diverse sources, including electronic health records, social media, and clinical trials . AI automates routine tasks like case processing, medical coding, and report generation, significantly reducing manual effort and human error. For instance, NLP algorithms can extract relevant information from unstructured data, transforming it into structured formats for analysis . Additionally, ML models can identify patterns and predict potential ADRs, facilitating early detection and proactive risk management .The integration of AI in pharmacovigilance leads to improved efficiency, accuracy, and compliance with regulatory standards. AI-powered systems ensure timely and consistent reporting of ADRs, enhancing patient safety and optimizing resource utilization .However, challenges remain, including data quality, algorithm transparency, and the need for human oversight in complex cases. Despite these hurdles, AI continues to revolutionize pharmacovigilance, offering a more proactive and data-driven approach to drug safety monitoring.