The purpose of the paper is to critically review the application of artificial intelligence (AI) to streamline the process of nanoparticle-based drug delivery systems, and in particular to address unresolved issues in therapeutic precision, safety, and clinical translation. The research uses the secondary research methodology and will synthesise evidence of peer reviews in the fields of nanotechnology, biomedical engineering, and computational modelling. This methodology allows the thorough assessment of various facets such as selection of nanoparticles material, optimization of particle size and shape, mode of surface functionalization, drug loading ability, controlled release prediction, targeting ligand design, analysis of cellular uptake pathway, prediction of pharmacokinetics, modeling of biodistribution, toxicity and biocompatibility, tumor microenvironment responsiveness, formulation optimization using AI, in vivo performance, real-time monitoring of delivery algorithms, personalized treatment, and optimization. According to the literature, it is constantly emphasised that AI will improve the accuracy of prediction, variability reduction, and speed up the design by combining multi-scale models, machine learning, and real-time monitoring. The results indicate that AI-based systems enhance drug encapsulation, can design targeted ligands, can simulate the biodistribution, and personalise treatment plans, thus closing the gap between laboratory innovations and clinical practice. Nevertheless, there are still issues in data heterogeneity, regulatory. ..
Keywords: Drug delivery, nanoparticle material, particle size, surface functionalization, targeting ligand, drug loading, controlled release, tumour microenvironment, pharmacokinetics prediction, biodistribution modelling
How to cite this article: Mohammad AAS, Mohammad SI, Vasudevan A, Al Oraini B, Alenazi SA.; AI-Driven Optimisation of Nanoparticle-Based Drug Delivery Systems. Int J Drug Deliv Technol. 2025;15(4): 1956-1993, DOI: 10.25258/ijddt.15.4.51