ABSTRACT: The integration of Artificial Intelligence (AI) with nanotechnology has emerged as a transformative approach in modern drug delivery systems. Nanotechnology-based drug delivery systems offer significant advantages such as targeted drug delivery, improved bioavailability, reduced toxicity, and controlled drug release. However, the design and optimization of nanocarriers remain complex due to the involvement of multiple physicochemical and biological parameters. Artificial Intelligence, including machine learning (ML), deep learning (DL), and predictive analytics, has demonstrated remarkable potential in addressing these challenges by accelerating nanoparticle design, predicting biological interactions, and optimizing formulation parameters. AI-driven models facilitate the identification of suitable nanomaterials, prediction of drug loading efficiency, and assessment of pharmacokinetic behavior. Furthermore, AI contributes to personalized medicine through patient-specific treatment strategies and real-time monitoring of therapeutic outcomes. Despite these advancements, several challenges including data quality, regulatory concerns, interpretability of algorithms, and ethical issues hinder widespread implementation. This review discusses the current applications of AI in nanotechnology-based drug delivery systems, highlights major challenges, and explores future perspectives for the development of intelligent and personalized nanomedicine. The convergence of AI and nanotechnology is expected to revolutionize pharmaceutical research and improve therapeutic efficacy across various disease conditions.