Artificial intelligence (AI) and machine learning (ML) are transforming pharmaceutical research and drug development. This review highlights the application of AI across the drug discovery pipeline, including multiomics data analysis, target identification, protein structure prediction, virtual screening, de novo drug design, retrosynthesis, ADMET prediction, and clinical trial optimization. Advanced deep learning models such as graph neural networks, transformers, and diffusion models have significantly improved molecular representation, interaction prediction, and novel compound generation. The integration of emerging approaches like federated learning and quantum machine learning is also discussed, particularly for overcoming data-sharing and computational limitations. Clinical examples of AI-designed drugs are examined to illustrate both successes and challenges in translating computational predictions into real-world outcomes. Despite substantial progress, issues such as model interpretability, data bias, and regulatory concerns remain critical barriers. Overall, AI is rapidly becoming a central driver of precision medicine by enhancing efficiency, reducing costs, and improving success rates in drug discovery. However, interdisciplinary collaboration and responsible governance frameworks are essential to fully realize its potential and ensure safe and effective implementation in pharmaceutical development.