"Artificial Intelligence in Pharmaceutical Formulation Development: Recent Trends and Future Prospectives"

Artificial intelligence (AI) is developing as a powerful tool for improving the efficiency, predictability, and reliability of pharmaceutical formulation development. Conventional trial-and-error methods are often laborious, expensive, and not good enough for addressing the complicated interactions among active pharmaceutical ingredients, excipients, process variables, and product performance. This review deliberate the application of AI, machine learning, deep learning, and artificial neural networks across major stages of formulation development, as well as preformulation studies, drug–excipient compatibility assessment, formulation optimization, process control, and dosage-form design. The role of AI in nanomedicine, liposomes, hydrogels, solid dosage forms, corneal drug delivery, and pharmaceutical three-dimensional printing is also examined. Moreover, the integration of AI with Qbd, Process Analytical Technology, real-time release testing, scale-up, and continuous manufacturing is emphasizing. AI-based models can assist prediction of solubility, dissolution, stability, particle characteristics, encapsulation efficiency, and drug-release behavior while reducing experimental requirements. Nevertheless, challenges involving data quality, model interpretability, external validation, regulatory acceptance, ethical considerations, and limited generalizability remain. Future developments are anticipate to focus on explainable AI, digital twins, automated laboratories, and closed-loop formulation systems. Overall, AI should be viewed as a complementary decision-support technology that improve, rather than exchange, experimental pharmaceutical development.