Active individuals and athletes generate large amounts of health and performance data, yet this information remains scattered across different systems such as motion analysis, metabolic testing, MRI, genetics, and wearables. Because these data are not integrated, coaches and clinicians often make decisions with limited visibility, leading to preventable injuries, suboptimal performance, and inefficient workflows. Our project addresses this problem by creating an AI-driven platform that connects all these data sources, automatically organizes and interprets them using digital-twin technology, and delivers practical, personalized recommendations. The goal is to give performance centers, academies, and clinics a single, intelligent tool that improves early detection of risk, supports better training decisions, and makes advanced diagnostics accessible and scalable.