Abstract：With advantages in high-dimensional data processing and complex nonlinear relationship mining, the machine learning technology provides a new approach for risk prediction and mechanism analysis in sports injuries. This paper focused on research progress in this field, discussing key roles of machine learning in injury risk identification, prediction, and early warning, and providing theoretical support for the development of a scientific and accurate sports injury prevention and control system. First, from the technical process of model construction, we summarized the common methods and technical characteristics of the three core links: feature engineering, model establishment, and optimization validation. Second, we compared the technical principles, application scenarios, and optimization paths of seven mainstream algorithms: decision tree, logistic regression, support vector machine, random forest, XGBoost, neural network, and hybrid learning, and describe their application effects in injury risk prediction. Finally, we analyzed the challenges faced by current research in terms of methodological heterogeneity, sample limitations, and model interpretability, and outline future research directions, including the construction of standardized systems, the improvement of data platforms, and the development of interpretable algorithms.
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