AI-Nanotechnology synergy: Revolutionizing precision drug delivery systems: A comprehensive review

Abstract
The traditional drug delivery technologies have serious limitations such as ineffective solubility of the drug, non-specific delivery, and less than optimal therapeutic responses. This review examines how artificial intelligence (AI) methods can be used to incorporate nanomaterial platforms, such as lipid nanoparticles, polymeric carriers, dendrimers, inorganic nanostructures, peptides, and hybrid systems to allow rational design and optimization of novel drug delivery. The AI models of supervised learning, deep neural networks, generative models, and reinforcement learning are used to overcome the classical methods of empirical exploration to conduct high-throughput screening, predicting biodistribution, tumor targeting efficacy, and controlled release kinetics. Among the most notable uses are in cancer therapy, delivery of genes (mRNA/siRNA), and stimuli-responsive systems, and improved delivery efficiency and performance in therapy have been shown in preclinical studies. There are still certain obstacles in the data standardization, model decipherability, manufacturing scalability, and regulatory endorsement, and joint endeavors are needed in the direction of clinical translation of AI-optimized nanomedicines. This is a synthesis of how AI can transform nanomedicine from a trial-and-error field to a predictive design that will enable precision therapeutics to be created and constructed to meet the most critical unmet needs in disease management.\r\n\r\n

Author
Diyar Salahuddin Ali

DOI
https://doi.org/10.1080/22297928.2026.2651096

ISSN
2230-7532

Publish Date: 2-Apr-2026