This study comprehensively addresses the use of machine learning and artificial intelligence applications in the prediction and management of sports injuries. Sports injuries are a major problem, especially in high performance sports. While traditional methods are usually based on observation and experience, the development of machine learning techniques in recent years has enabled data-based analysis.

Among machine learning models, techniques such as decision trees, support vector machines (SVM), artificial neural networks (ANN), convolutional neural networks (CNN) and recurrent neural networks (RNN) are effectively used in predicting sports injury risks. Especially in sports with high injury risk such as soccer, basketball and track and field, studies have demonstrated the accuracy of these models.

Biomechanical data from wearable sensors, GPS data and AI-assisted analysis tools play an important role in assessing athletes' performance and identifying injury risks. In particular, biomarker data such as heart rate variability (HRV) and movement pattern analysis contribute to the development of individualized injury prevention strategies.

Machine learning also plays an important role in post-injury recovery and follow-up processes. AI-powered feedback systems and motion tracking devices optimize the rehabilitation process of athletes and reduce the risk of re-injury. The use of these technologies offers great potential to protect athlete health and improve performance.