The pharmaceutical industry continuously seeks innovative strategies to enhance drug solubility and stability, which are critical for effective drug delivery and therapeutic efficacy. The integration of Artificial Intelligence (AI) in pharmaceutical sciences offers transformative potential for addressing these challenges through AI-driven predictive modeling. This research employs AI techniques, particularly machine learning and deep learning, to predict the behavior of drug molecules within various formulations, aiming to optimize both solubility and stability. AI models analyze vast datasets from preclinical experiments to identify patterns that can predict how different formulations affect drug properties. This study highlights the ability of AI to reduce experimental workload by forecasting interactions between drug molecules and excipients, thereby enhancing the efficiency of the formulation process and potentially reducing development costs. The use of neural networks to model physicochemical properties and their implications on solubility and stability demonstrates that AI can recommend innovative combinations of materials and techniques that improve formulation outcomes. This research underscores the increasing importance of AI in pharmaceutical development, providing a foundation for further innovations in drug delivery systems.