Artificial Intelligence in Pharmacovigilance: A Narrative Review of Global Regulatory Perspectives, Technological Evolution, and Emerging Challenges

Pharmacovigilance plays a key role in public health-it helps us spot, assess, and prevent adverse drug reactions throughout a drug’s life on the market. This review takes a closer look at how AI is reshaping pharmacovigilance. It covers what’s new in AI-driven tools, what global regulators think, where the technology stands now, and what hurdles still remain for drug safety monitoring. Review was conducted using peer-reviewed articles, regulatory reports, and official guidance from databases: PubMed, Scopus, Google Scholar, WHO, UMC, FDA and EMA. The literature was reviewed to assess AI drug safety, how regulators view these new tools, and the hurdles everyone’s facing. AI, whether it’s is machine learning, natural language processing or deep learning methods, is a facilitator of drug safety monitoring. Global regulatory authorities have increasingly integrated AI into PV frameworks to improve data processing efficiency and regulatory decision-making. However, challenges related to algorithmic bias, data, quality, model interpretability and causality assessment continue to limit widespread implementation. New approaches like explainable AI, federated learning, and multimodal systems have the potential to transform future PV systems and global safety surveillance. The successful integration of AI into PV requires robust regulatory frameworks, transparency, ethical governance, and continued human oversight to ensure reliable and patient-centered drug safety monitoring.