The revolutionary potential of machine learning (ML) in pharmacovigilance the early detection of adverse drug reactions (ADRs) in the digital era is examined in this study. It is difficult to promptly identify and address adverse drug reactions (ADRs) using traditional pharmacovigilance techniques. This study suggests an approach for combining structured and unstructured data sources for reliable ADR detection that makes use of machine learning. Many machine learning (ML) algorithms, including ensemble methods and neural networks, are used and contrasted with traditional techniques. Problems are tackled, such as ethical issues and the dependability of the data. Case studies present real-world implementations and shed light on how well ML models work. The study addresses the interpretability of these models, how to incorporate them into the current pharmacovigilance systems, and how data scientists and healthcare practitioners might work together. Upcoming developments and legal issues are highlighted in future directions. This study highlights how ML has the ability to completely transform pharmacovigilance by providing a proactive and more effective method of detecting ADRs and indicating a bright future for medication safety in the digital era.