One of the main advantages of applying artificial intelligence in medical delivery systems is its capacity to handle and evaluate large amounts of data effectively. Artificial intelligence algorithms can use large amounts of data to identify trends in medication formulations that might not be readily apparent using more traditional approaches ( ). Artificial intelligence methods can examine data-driven sources such as chemical structures, physicochemical properties, and biological activity data to expose hidden relationships. Furthermore, enabling the forecast of medication durability in different storage conditions, the solubility of pharmaceuticals in various formulations, and the release of kinetics from other delivery systems is the ability of machine learning models to grasp complex connections and non-linear interdependencies ( ). Using pattern and trend analysis, AI can thus enhance medicine delivery systems, influencing ) greater effectiveness, safety, stability, solubility, and release kinetics.