This chapter explores how artificial intelligence (AI) is transforming the entire drug discovery and development process, from target identification and molecular design to drug repurposing and clinical optimization. By incorporating machine learning (ML), deep learning (DL), and natural language processing (NLP) techniques, AI facilitates faster and more cost-effective drug innovation. The chapter discusses AI’s role in virtual screening, molecular docking, and quantitative structure–activity relationship (QSARquantitative structure–activity relationship (QSAR) modeling) modeling, which help streamline hit identification and lead optimization. Advanced computational models such as convolutional neural networks (CNNs), hybrid neural networks (HNNs), and transformer architectures are enhancing the prediction of protein–ligand binding affinity and supporting de novo molecular design. AI-driven methods in multiomic and computational target identification, along with drug repurposing, are also highlighted for their contributions to personalized medicine and discovering new therapeutic applications. Overall, these developments demonstrate how AI accelerates the creation of safe, effective, and precisely targeted treatments, transforming the future of biomedical research and pharmaceutical innovation.