The integration of artificial intelligence (AI) and machine learning (ML) is fundamentally transforming pharmaceutical sciences, shifting drug formulation and nanocarrier design from traditional empirical approaches toward predictive, data-driven methodologies. By enabling the analysis of large, complex datasets, AI technologies are accelerating decision-making, improving formulation efficiency, and supporting the development of more effective therapeutic systems. Despite these advances, the successful clinical translation of advanced nanomedicines, including polymeric nanoparticles and mRNA-lipid nanoparticle platforms, remains limited by challenges such as biological barriers, highly sensitive formulation parameters, scalability issues, and the limited interpretability of many computational models. This review provides a comprehensive overview of AI applications throughout the pharmaceutical development lifecycle. It explores how classical machine learning algorithms and deep learning architectures optimize conventional dosage forms, enhance formulation development, and enable the rational design of targeted nanocarriers. Particular emphasis is placed on predicting critical quality attributes, encapsulation efficiency, physicochemical properties, drug-release behavior, therapeutic efficacy, and early-stage nanotoxicity. Furthermore, we critically assess the regulatory considerations, manufacturing constraints, data quality issues, and tumor microenvironment heterogeneity that continue to impede bench-to-clinic translation. Ultimately, overcoming these challenges requires moving beyond isolated algorithmic optimization toward an integrated framework that combines computational intelligence, robust experimental validation, and continuous clinical feedback. Such a synergistic approach is expected to drive the next generation of precision nanomedicine and facilitate the safe and effective translation of AI-enabled pharmaceutical innovations into clinical practice.
