AI‐Driven Microphysiological Systems for Advancing Nanoparticle Therapeutics

Nanoparticles (NPs) play a critical role in applications ranging from medicine to materials science, requiring precise control of properties, such as size, shape, and surface chemistry. The synthesis and evaluation of NPs, however, remain resource‐intensive processes. Machine learning (ML) offers a powerful tool to optimize NP synthesis by predicting key parameters and improving efficiency. Coupled with microphysiological systems (MPS), such as organ‐on‐a‐chip models, ML enables detailed studies of NP transport, toxicity, and therapeutic performance in physiologically relevant environments. This review highlights recent advancements in ML‐assisted NP synthesis, strategies for dataset acquisition, and case studies on various NP types. The integration of ML with MPS is examined for its potential in high‐throughput screening and experimental optimization. Current challenges and future directions for leveraging ML and MPS in NP research are also discussed, aiming to streamline development and enhance predictive accuracy.