Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery

ABSTRACT The integration of artificial intelligence (AI), and machine learning (ML) with nanomedicine represents a transformative approach in cancer drug delivery, offering solutions to long‐standing challenges such as formulation optimization, targeted delivery, and predictive efficacy. While nanocarriers like liposomes, dendrimers, and polymeric nanoparticles have enhanced site‐specific delivery, their development remains constrained by trial‐and‐error formulation methods. In this review, we critically examine how ML algorithms, including support vector machines, artificial neural networks, and deep learning models which are widely being applied to streamline nanoparticle design, predict drug release profiles, and personalize therapeutic regimens based on patient data. We highlight case studies where ML has accelerated nanoparticle fabrication, improved internalization modeling, and supported the development of self‐driving labs for formulation screening. We also discuss computational workflows, data handling strategies, and the operational principles behind commonly used ML models in pharmaceutical research. Importantly, the review addresses key limitations such as data scarcity, lack of model interpretability, and regulatory hurdles that hinder clinical translation. By synthesizing recent advances and identifying ongoing gaps, this article offers a roadmap for future interdisciplinary research at the interface of AI and nanomedicine.