Sports analytics is increasingly incorporating artificial intelligence (AI) to improve athlete monitoring and injury prevention. Creating personalized AI-driven performance assessments with real-time insights into athletes’ physiological conditions has been a primary focus. Unlike traditional method, artificial intelligence models may analyze dynamic, complex data to create tailored suggestions that enhance training load while preventing overtraining. Current research indicates that these personalized AI models hold significant promise for solving critical gaps in athlete monitoring, particularly in injury prediction models. However, integrating multimodal data sources—namely, biomechanical, physiological, and environmental data—into coherent AI models remains complex and difficult. Particularly reinforcement learning and neural networks, advances in machine learning methods have showed promise in producing adaptive models that may continuously change depending on the special requirements of individual athletes. Moreover, wearable and sensor data-based prediction models have proven efficacy in seeing early signs of weariness or injury risk, so allowing proactive therapies. From applications of these AI-driven techniques, elite sports have already benefited in training management and injury avoidance. Though progress has been made, further research is needed to enhance data collection methods, mix psychological assessments, and expand artificial intelligence applications at many levels. Artificial intelligence seems to have promising future prospects in sports analytics in constructing full systems that provide real-time, tailored insights into athlete performance, therefore guaranteeing both optimal performance and low injury risks.