Artificial intelligence (AI) fundamentally transforms the pharmaceutical industry across its entire value chain. This review examines the multifaceted applications of AI in pharmaceuticals, from accelerating drug discovery and development to enhancing manufacturing processes and post-market surveillance. We analyze how AI technologies, such as deep learning, have dramatically improved target identification, lead optimization, and clinical trial efficiency while reducing development timelines and costs. Notable platforms, including AlphaFold2, PandaOmics, and Chemistry42, demonstrate AI’s ability to overcome traditional bottlenecks in pharmaceutical R&D. In manufacturing and supply chain management, AI-powered predictive maintenance and demand forecasting systems have produced quantifiable improvements in operational efficiency. Post-market applications include enhanced pharmacovigilance, personalized medicine optimization, and targeted marketing strategies. Despite these advancements, significant challenges concerning data quality, algorithmic explainability, regulatory frameworks, and ethical considerations remain. Integrating AI with complementary technologies, such as quantum computing and autonomous laboratory systems, promises further innovation, particularly in addressing unmet medical needs in rare diseases, antimicrobial resistance, and pandemic preparedness. This review highlights the necessity of collaborative approaches among industry stakeholders, regulatory authorities, and academic institutions to fully realize AI’s transformative potential pharmaceuticals.