Introduction and Aim: Sports injuries are among the major challenges affecting athletes’ health and performance. Due to their multifactorial and dynamic nature, injury risk prediction requires simultaneous consideration of individual, training-related, equipment-related, and environmental factors. Most existing models primarily focus on athlete-related data and pay limited attention to the interactions between athletes, equipment, and the surrounding environment. Therefore, this study aims to develop and validate an athlete–equipment–environment digital twin for predicting injury risk in sports facilities.

Methodology: This applied-developmental study will employ predictive modeling based on longitudinal and multimodal data. Data on individual characteristics, injury history, training load, physiological and biomechanical indicators, equipment status, and environmental conditions will be collected and temporally synchronized. Following data preprocessing, single-source and multimodal predictive models will be developed, and a dynamically updating digital twin will be established. Model performance will be evaluated using discrimination, calibration, and external validation measures.

Results: The proposed multimodal digital twin is expected to demonstrate improved predictive and calibration performance compared with models based solely on athlete-related data. The relative contribution of athlete, equipment, and environmental factors to changes in injury risk will also be determined, together with the model’s generalizability to independent datasets.

Conclusion: The proposed digital twin may provide an innovative framework for predicting and managing injury risk by dynamically representing interactions among athletes, equipment, and the environment. This approach may facilitate a transition from reactive injury management toward intelligent, individualized, and data-driven injury prevention in sports facilities.