1Associate Professor, Amity Institute of Public Health and Hospital Administration, Amity University, Noida, India
ORCID: https://orcid.org/0000-0002-2299-3475
2Department of Mathematics, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India
Email: mayavel.maya@gmail.com
3Head of the Department & Associate Professor, CSE (Data Science), Visvesvaraya College of Engineering and Technology, Ranga Reddy, Hyderabad, Telangana, India
Email: srikanth.kalari@gmail.com
4Technical Manager, Software Engineer, HCL Tech, Cary, North Carolina, NC, 27519, USA
Email: nikhilppsm@gmail.com
5Assistant Professor, School of Computer Science & Engineering, Ramdeobaba University, Nagpur, Maharashtra, India
Email: kadurk@rknec.edu
6Department of Mathematics, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India
Email: mayavel.maya@gmail.com
The integration of artificial intelligence (AI) and machine learning (ML) with nanotechnology has opened new frontiers in precision medicine and advanced therapeutic delivery systems. Traditional drug delivery approaches often suffer from limitations such as poor targeting efficiency, systemic toxicity, low bioavailability, and unpredictable pharmacokinetics. Nanocarrier-based drug delivery systems including liposomes, polymeric nanoparticles, dendrimers, and lipid nanoparticles have emerged as promising solutions to overcome these challenges by enabling controlled and targeted release of therapeutic agents. However, the design, optimization, and clinical translation of nanocarrier systems remain complex due to the large number of variables influencing nanoparticle behavior within biological environments. Artificial intelligence and machine learning technologies provide powerful computational tools capable of analyzing large biomedical datasets, predicting nanoparticle interactions, and optimizing nanocarrier design parameters for improved therapeutic outcomes. This study explores the integration of AI and ML models with nanocarrier-based drug delivery platforms to develop predictive and targeted therapeutic systems. The proposed framework combines data-driven modeling, nanoparticle characterization, and predictive analytics to improve drug targeting efficiency and reduce adverse effects. Machine learning algorithms such as neural networks, support vector machines, and reinforcement learning are utilized to analyze physicochemical properties of nanoparticles, predict drug release profiles, and optimize targeting strategies. The study further evaluates how AI-enabled predictive modeling can assist in personalized medicine by tailoring therapeutic delivery based on patient-specific biological and genomic characteristics. The results indicate that AI-integrated nanocarrier systems significantly enhance targeting accuracy, therapeutic efficiency, and drug delivery precision compared to conventional delivery methods. Additionally, the integration of predictive analytics allows early identification of potential toxicity and pharmacokinetic challenges during the drug development process. The findings highlight the transformative potential of AI-driven nanomedicine in advancing next-generation therapeutic systems. By combining nanotechnology with intelligent computational frameworks, healthcare systems can move toward more efficient, personalized, and predictive treatment strategies for complex diseases such as cancer, neurological disorders, and chronic inflammatory conditions.
Keywords: Artificial Intelligence, Machine Learning, Nanocarrier Systems, Targeted Drug Delivery, Nanomedicine, Predictive Therapeutics, Precision Medicine.
How to cite this article: Sinha AP, Mayavel P, Srikanth K, Gurram NT, Kadu RK, Mayavel P. Artificial Intelligence & Machine Learning Integrated Nanocarrier Systems for Predictive and Targeted Therapeutic Delivery. Int J Drug Deliv Technol. 2026;16(8s): 305-314; DOI: 10.25258/ijddt.16.8s.42
Source of support: Nil.
Conflict of interest: None