The Convergence of Polymer Science and Predictive Modeling for Noninvasive Glucose Monitoring

The global effort to manage diabetes effectively is driving continuous innovation in glucose monitoring devices. While current systems have improved patient care, persistent challenges with sensor stability and invasiveness highlight the need for advanced, patient-friendly technologies. A particularly promising frontier is emerging from the convergence of advanced polymer science and artificial intelligence (AI), opening new pathways for noninvasive biosensing. This feature review provides a comprehensive overview of polymer-based "hardware", such as molecularly imprinted polymers (MIPs), conductive polymer hydrogels (CPHs), and functional coatings, which offer robust and biocompatible alternatives to traditional enzyme-based sensors. Concurrently, we examine (AI) "software", including machine learning and predictive modeling, which enable reliable interpretation of complex biosignals for real-time glucose monitoring. Furthermore, this review highlights critical challenges in scalability, long-term in vivo stability, regulatory approval, and clinical adoption, while discussing strategies for successful translation into pharmaceutical technology and medical devices. By mapping the current landscape and future directions, this review aims to guide research toward the next generation of intelligent, patient-centric, noninvasive glucose monitoring platforms.