Injury prevention in sports has gained significant attention due to the increasing physical demands placed on athletes. A data-driven approach is seen as essential in mitigating injury risks. The integration of biomechanics, wearable technology, and advanced data analytics presents a promising solution for addressing this issue. Wearable devices equipped with sensor technology can collect real-time physiological and biomechanical data. This information, processed through machine learning models, enables the identification of injury risks and provides personalized insights based on athletes’ unique profiles. Research has revealed that wearable technology and machine learning models can predict potential injuries by identifying deviations in biomechanical patterns that traditional methods may overlook. Machine learning algorithms such as unsupervised learning models have proven effective in detecting anomalies and clustering high-risk movements. Despite the advancements in these technologies, a gap remains in translating data into actionable, effective injury prevention strategies. It is suggested that enhancing machine learning models for real-time, personalized injury prediction will bridge this gap. The continuous refinement of predictive algorithms as more data is gathered holds potential for improved accuracy in injury prevention. Real-time feedback systems and the integration of external workload metrics with internal physiological data also contribute to mitigating injury risks. However, challenges such as cost, accessibility, and technical expertise need to be addressed to broaden the applicability of these technologies. Future research should focus on improving the affordability of wearables, enhancing model accuracy, and integrating more sophisticated physiological markers to create even more effective injury prevention strategies.