Machine Learning ‐ Driven Polysaccharide ‐ Based Hydrogels: Intelligent Design and Precision Therapeutics for Oral Wound Repair

Abstract Oral mucosal wound healing presents considerable challenges due to its unique moist microenvironment, dynamic mechanical stress, and intricate microbial communities. Polysaccharide‐based hydrogels have garnered increasing attention as promising candidates for advanced oral repair materials, owing to their ability to mimic the extracellular matrix (ECM), their tunable degradation kinetics, and their multimodal responsiveness. Despite these advantages, the clinical translation of such material remains limited, largely due to its inefficient molecular design strategies, inadequate adaptability to dynamic physiological conditions, and a lack of personalized therapeutic solutions. Recent advances in machine learning (ML) offer a powerful toolkit to overcome these limitations. By integrating material omics data with clinical feature information, ML enables the development of predictive models to predict and guide the rational design of intelligent hydrogels. Coupled with personalized treatment algorithms, this approach holds significant potential to tailor the functional performance of hydrogels to the unique clinical needs of individual patients. In this review, the mechanistic foundations of polysaccharide hydrogels is comprehensively elucidated, analyze the role of ML in optimizing their properties and enhancing clinical translation, and propose a conceptual framework for advancing oral wound repair strategies.