Volume-1 ,Issue-4, November-2025

Drug-drug interactions (DDIs) are a major problem in clinical practice and pharmaceutical development; they frequently result in decreased therapeutic efficacy, adverse drug reactions, and higher healthcare costs. Conventional approaches to DDI identification, such as in vitro assays, animal studies, and clinical trials, are labor-intensive, expensive, and limited in their ability to detect rare or population-specific interactions. By integrating heterogeneous data sources, such as chemical structures, pharmacokinetic and pharmacodynamic profiles, electronic health records, and natural language processing. Early-stage drug discovery, clinical trial optimization, regulatory compliance, and post-marketing surveillance are all supported by AI-driven methods, which also integrate pharmacogenomic and multi-omics data to enable customized DDI risk assessment and precision medicine. This review highlights the crucial role that AI plays in improving medication safety, lowering financial burdens, and facilitating patient-centered therapeutic approaches. It also explores the existing approaches, industrial uses, and future possibilities of AI in DDI prediction and management.

Keyword: Artificial Intelligence, Drug–Drug Interaction, Machine Learning, Deep Learning, Pharmacovigilance, Pharmaceutical Industry, Predictive Toxicology