Volume-1 ,Issue-4, November-2025

By facilitating data-driven innovation throughout the whole drug development lifecycle—from discovery and preclinical research to manufacturing, regulatory compliance, and patient engagement—artificial intelligence (AI) is transforming the pharmaceutical sector. Target identification, virtual screening, in silico ADMET prediction, clinical trial optimization, quality control, and pharmacovigilance are all made easier by AI technologies, such as machine learning, deep learning, and natural language processing. Efficiency, accuracy, and decision-making are improved throughout end-to-end workflows when AI is integrated with reliable data infrastructures, cloud computing, IoT, and interoperable platforms. To guarantee the safe, dependable, and moral use of AI, however, issues including data quality, regulatory uncertainties, cybersecurity threats, ethical considerations, and workforce skill gaps must be resolved. Maintaining reproducibility, transparency, and patient safety requires human-AI cooperation, explainable AI (XAI), validation frameworks, and Good Machine Learning Practice (GMLP) standards. New developments that have the potential to further revolutionize pharmaceutical operations and customized medicine include generative AI, digital twins, autonomous laboratories, and interaction with Web3 and the metaverse. To fully realize AI's potential, promote innovation, and enhance therapeutic outcomes, interdisciplinary collaboration, unified worldwide guidelines, and strategic execution are essential.

Keyword: Artificial Intelligence, Pharmaceutical Industry, Drug Discovery, Clinical Development, Pharmacovigilance, Machine Learning