By greatly cutting the time and expense needed to find and develop novel treatments, artificial intelligence (AI) integration in drug development is transforming the pharmaceutical sector. Conventional approaches of drug discovery are time-consuming, resource-intensive, and frequently show poor success rates. Deep learning, machine learning, and computational drug design—among other AI-powered technologies—are allowing fast screening of chemical compounds, drug-target interaction prediction, and very precise lead compound optimization. From theoretical molecules to practical therapeutic prospects, advanced models—including generative artificial intelligence, reinforcement learning, and neural networks—improve molecular docking, de novo drug design, and biomarker discovery, hence hastening the change from theory to reality. By means of predictive analytics, patient classification, and real-time monitoring, AI-driven methods also simplify preclinical and clinical trial phases, thereby enhancing drug efficacy and safety. Adoption of artificial intelligence in pharmaceutical research not only speeds up development but also opens the path for customized treatment, therefore lowering negative effects and enhancing patient outcomes. To fully maximize artificial intelligence’s promise in drug discovery, though, issues including data quality, ethical considerations, and regulatory compliance must be resolved. This paper investigates the transforming potential of artificial intelligence in accelerating pharmaceutical innovation, stressing recent developments, difficulties, and future directions in drug development driven by AI.