Artificial Intelligence (AI) is increasingly reshaping drug discovery and development by offering new computational capabilities that significantly enhance efficiency, accuracy, and innovation. This comprehensive review discusses the evolving role of AI across various stages of pharmaceutical R&D—from early target identification and validation to lead optimization, preclinical assessment, and clinical trials. With the growing complexity and costs associated with traditional drug development pipelines, AI presents powerful alternatives through machine learning (ML), deep learning (DL), and natural language processing (NLP) tools that enable rapid data analysis, compound generation, and predictive modeling. In target discovery, AI algorithms analyze vast omics datasets to identify novel biological targets, while virtual screening models streamline high-throughput screening of chemical libraries with improved hit rates