Machine learning–enabled intelligent hydrogels: applications and future perspectives

Hydrogels are increasingly used in flexible electronics, biomedicine, and soft robotics due to their tissue-like mechanics, high water content, stimuli responsiveness, and programmable structures. At the same time, machine learning (ML) and deep learning (DL) are accelerating hydrogel design, signal processing, and intelligent control. This review presents an integrated framework linking hydrogel materials, flexible/wearable sensing, and ML-enabled analysis and optimization, distinguishing it from previous reviews on general hydrogel design or isolated ML applications. Recent progress is summarized in flexible sensing, health monitoring, drug delivery, cell behavior analysis, and soft robotics. Current challenges and future opportunities are discussed, particularly in data standardization, model generalizability, multimodal fusion, and closed-loop intelligent systems.