In the age of big data and artificial intelligence (AI), post-market surveillance and pharmacovigilance face previously unheard-of difficulties. The 94% median underreporting rate and delayed signal detection are two major drawbacks of traditional pharmacovigilance systems that rely on spontaneous adverse drug reaction (ADR) reporting. This analysis looks at how pharmacovigilance procedures are changing along the whole value chain, from data collection to regulatory reporting, as a result of AI and machine learning (ML) technology. Natural language processing (NLP), deep learning, and predictive analytics are used by contemporary pharmacovigilance systems to examine enormous datasets from wearable technology, clinical trials, social media, and electronic medical records. Real-time monitoring and early safety signal identification are made possible by these technologies. Notable implementations include deep-learning techniques that achieve over 75% accuracy in processing individual case safety reports (ICSRs) and the vigiMatch algorithm from the Uppsala Monitoring Centre, which processes 50 million report pairings per second for duplicate identification. Every one of the four main types of big data analytics—prescriptive, exploratory, descriptive, and predictive—brings special skills to pharmacovigilance activities. Beyond signal detection, AI is used in industrial quality control, medicine repurposing, and resource allocation optimisation. Significant obstacles still exist, though, such as problems with data quality, privacy, standardisation, and the requirement for human monitoring in the "human-in-the-loop" method. This review examines international regulatory strategies and talks about the moral ramifications of pharmacovigilance powered by AI. In this revolutionary age of healthcare innovation, future perspectives place a strong emphasis on combining human expertise with cutting-edge technology to improve public health protection and advance international drug safety standards.