194INTELLIGENT AUTOMATION IN PHARMACO VIGILIANCE; ADVANCING GLOBAL DRUGSAFETY STANDARDSPharmacovigilance (PV) plays a vital role in maintaining the safety, efficacy, and positive benefit–risk balance of medicines across their entire lifecycle. It encompasses systematic efforts to detect, evaluate, understand, and prevent adverse drug reactions (ADRs) and other safety concerns associated with pharmaceutical use. With the growing globalization of healthcare and the expansion of data-driven systems, the scale and complexity of safety information have risen sharply. Conventional PV methods, which rely heavily on manual review and reporting, are increasingly inadequate to manage these expanding datasets. Such traditional systems often face challenges like delayed signal detection, data inconsistencies, and human error, all of which may jeopardize timely regulatory compliance and patient safety. To overcome these limitations, pharmacovigilance is undergoing a major digital transformation powered by emerging technologies. The integration of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Robotic Process Automation (RPA) has begun reshaping how safety data are gathered, processed, and interpreted. These innovations enable faster analysis, improved accuracy, and more proactive safety monitoring. However, adopting automation in PV also brings new challenges. Uncertainties regarding regulatory acceptance, concerns over data protection, technical barriers to system integration, and ethical issues surrounding algorithmic transparency and accountability require careful consideration. Striking the right balance between automation and expert oversight remains essential. A hybrid approach merging human expertise with intelligent technologies offers the most sustainable path forward. This thesis explores the evolving role of smart automation in pharmacovigilance, analysing its enabling technologies, practical applications, and associated regulatory and ethical dimensions. Drawing from literature, case studies, and real-world experiences, it discusses how automation can strengthen operational efficiency, strategic decisionmaking, and patient-centric safety practices. Ultimately, it concludes that the responsible use of automation can make pharmacovigilance more predictive, responsive, and globally harmonized, driving the next era of intelligent drug safety managementlavanyaniky@gmail.comResearch1622026Pharmacovigilance, Adverse Drug Reactions (ADRs), Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP)Bathinapatla Hemalatha, Lavanya A*, Dr. Ramesh Reddy KKrishna Teja Pharmacy College, Chadalawada Nagar, Tirupati - 517506, Andhra Pradesh, India,Krishna Teja Pharmacy College, Chadalawada Nagar, Tirupati - 517506, Andhra Pradesh, India