• The increasing availability of vast amounts of healthcare data and the advancements in analytics techniques have opened new avenues for drug safety surveillance. This study investigates the application of big data analytics in this domain, focusing on data aggregation, signal detection, real-time monitoring, signal validation and prioritization, comparative effectiveness studies, and data integration and collaboration. It explores how diverse data sources, such as electronic health records (EHRs), insurance claims databases, social media, and patient forums, can be integrated to obtain a comprehensive view of drug usage patterns and potential safety issues. Advanced analytics techniques, including data mining, machine learning, and natural language processing, are examined for their ability to automatically detect potential drug safety signals. The study emphasizes the significance of real-time monitoring for the rapid identification of emerging drug safety issues and the role of signal validation and prioritization in focusing resources on critical signals. Furthermore, it explores how big data analytics enables comparative effectiveness studies to assess the safety profiles of different interventions. The research also highlights the importance of data integration and collaboration in enhancing the understanding of drug safety signals and promoting collective decision-making among stakeholders.