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.

Conventional oral and injectable drug administration still struggles with unstable plasma levels, weak targeting, and considerable systemic toxicity, problems that become especially acute in chronic disease management. Implantable and wearable biomedical devices offer one path around these limits, yet device-only platforms continue to fall short on drug loading, release control, and protection of fragile therapeutics. Integrating nanoparticle-based biomaterials into such devices has therefore moved from a research curiosity to a serious clinical strategy. As a result, understanding the design principles, translational challenges, and clinical potential of these hybrid platforms has become increasingly important. This review provides a comprehensive assessment of four major nanoparticle families—polymeric carriers (PLGA, chitosan, and micelles), lipid-based vehicles (liposomes, SLNs, and NLCs), inorganic systems (gold, mesoporous silica, iron oxide, and calcium phosphate), and hybrid composites—focusing on how their physicochemical properties govern drug encapsulation, release behavior, and tissue compatibility. These classes are then linked to specific implantable formats such as drug-eluting stents, nano-enabled scaffolds, and reservoir depots, and to wearable formats including transdermal patches, microneedle arrays, biosensor-coupled patches, and patient-actuated devices. A dedicated section addresses stimuli-responsive release driven by pH, enzymes, temperature, and electrical or magnetic fields, alongside closed-loop platforms that pair real-time biosensing with on-demand dosing. Surface engineering strategies, ligand targeting, antifouling coatings, antimicrobial layers, and immune-modulating chemistries are also discussed, together with the central translational hurdles: long-term stability, foreign body response, scale-up, sterilization, and regulatory classification of combination products. Finally, the review outlines near-term directions, including AI-driven dosing, 4D bioprinting, biomimetic nanocarriers, gene therapy delivery, and bioresorbable electronics, that together suggest where these hybrid platforms are likely to mature next.