Artificial intelligence (AI) is transforming drug discovery by dramatically improving efficiency, lowering costs, and raising the rate of success. AI-powered algorithms scan enormous biological datasets, such as genomics and proteomics, to discover disease-related targets and forecast therapeutic interactions. This AI-supported process speeds up drug research, streamlining the drug development pipeline and heightening approval success rates. AI further helps predict pharmacokinetics, toxicity, and lead compound optimization, reducing costly and time-consuming experimental processes. In addition, AI-based systems assess real-world patients' data to offer personalized drug choices and optimize treatment effectiveness as well as compliance. This review exhaustively discusses AI applications in drug discovery, PK/PD studies, process optimization, and drug delivery dosage form development and raises related challenges as well as future directions for research.