Volume-1 ,Issue-2, September-2025

By identifying, evaluating, and averting adverse drug reactions (ADRs), pharmacovigilance (PV) is crucial to guaranteeing the safety and effectiveness of medications. Given the growing complexity and volume of healthcare data, traditional signal detection techniques are frequently inadequate. Advanced analytics like machine learning and Bayesian modeling may now be integrated with a variety of data sources, including spontaneous reporting systems, electronic health records, registries, literature, and patient-reported platforms, thanks to software-assisted and AI-driven tools. These solutions facilitate regulatory compliance and enhance the prompt and accurate identification of safety alerts. There are still issues with data quality, computational biases, moral dilemmas, and the requirement for professional supervision. Predictive, patient-centered, and globally standardized pharmacovigilance systems that use real-world data and cloud-based platforms for early risk prediction are the focus of emerging trends. This paper highlights the revolutionary potential of technology in improving drug safety by giving an overview of current software, methodology, regulatory frameworks, difficulties, and future possibilities.

Keyword: Pharmacovigilance, Signal Detection, Adverse Drug Reactions, AI, Machine Learning, Real-World Evidence, Safety Monitoring