Abstract
Nanomaterials have revolutionized drug delivery by enabling precise control over solubility, stability, circulation time, and targeted release, yet translation from bench to bedside remains challenging due to complex synthesis, unpredictable biological interactions, and regulatory hurdles. Recent advances in artificial intelligence (AI) and big data analytics offer powerful solutions to these bottlenecks by integrating multidimensional datasets-encompassing physicochemical characterization, pharmacokinetics, omics profiles, and preclinical outcomes-to generate predictive models for rational nanocarrier design. Machine learning and deep learning approaches enable the prediction of key parameters such as particle size, drug loading efficiency, and biodistribution, while generative algorithms explore novel chemistries and architectures optimized for specific clinical applications. Nanoinformatics platforms and large-scale data repositories further enhance reproducibility and cross-study comparisons, supporting regulatory science and accelerating clinical translation. This review provides a comprehensive overview of nanomaterial-based drug delivery systems, highlights AI-driven strategies for predictive modeling and optimization, and discusses translational and regulatory perspectives. By bridging nanotechnology, computational modeling, and precision medicine, AI-assisted nanomaterial design has the potential to transform drug delivery into a more efficient, reproducible, and patient-centered discipline.