Artificial Intelligence in Pharmaceutical Formulation Development

Artificial intelligence (AI) is increasingly transforming pharmaceutical formulation development by enabling data-driven decision-making, predictive modeling, and efficient optimization of complex formulation and manufacturing processes. Conventional formulation approaches, although scientifically robust, often rely on extensive trial-and-error experimentation and face limitations when addressing nonlinear interactions, multicomponent systems, and scale-up challenges. Recent advances in machine learning (ML), deep learning (DL), artificial neural networks, and hybrid AI models have demonstrated significant potential in overcoming these constraints. This review critically examines recent developments (2023–2025) in the application of AI across pharmaceutical formulation development, encompassing preformulation studies, drug–excipient compatibility prediction, formulation design and optimization, process parameter control, and integration with Quality by Design (QbD) and Process Analytical Technology (PAT) frameworks. The role of AI in novel drug delivery systems, scale-up, and smart manufacturing is discussed, along with regulatory considerations related to model validation, data integrity, and interpretability. Representative case studies highlight the advantages of AI-enabled approaches over conventional methods in reducing development time, minimizing experimental burden, and enhancing product quality. Finally, future perspectives including autonomous formulation laboratories, digital twins, personalized medicine, and continuous manufacturing are outlined, emphasizing AI’s growing importance in modern pharmaceutical sciences.