Pharmacovigilance is crucial for ensuring the safety and efficacy of pharmaceuticals because it identifies, assesses, and avoids adverse drug reactions (ADRs). New possibilities for enhancing medication safety monitoring have been made possible by artificial intelligence (AI) and the exponential growth of digital data. Despite their usefulness, traditional pharmacovigilance systems sometimes have shortcomings, including underreporting, delayed signal detection, and issues with large datasets. By combining AI approaches and big data analytics, these challenges can be addressed through real-time monitoring, early signal recognition, and predictive analysis of potential drug-related hazards. Automating the processing of adverse drug reaction (ADR) reports, identifying hidden trends, and predicting patient-specific risks based on clinical history and demographics are all made possible by machine learning and natural language processing (NLP) algorithms. Despite these advantages, several problems persist, including algorithmic and data privacy difficulties. transparency, ethical considerations, and adherence to the law. AI and pharmacovigilance are transforming drug safety systems into proactive, data-driven frameworks that are transforming modern healthcare. AI-powered pharmacovigilance eventually has the potential to lower prescription risks, enhance patient outcomes, and boost public trust in pharmaceutical care by making judgments more rapidly, precisely, and empirically. In order to enable real-time surveillance, early ADR identification, and predictive analytics, this study examines how AI-driven tools can close significant gaps in current pharmacovigilance methods. The study concludes by emphasizing that integrating AI into pharmacovigilance frameworks promises more proactive, accurate, and effective pharmaceutical safety monitoring, with the goal of better protecting public health, as new therapeutic agents are developed and real-world data becomes more accessible.