<p>Effective training load management is crucial for optimizing performance and minimizing injury risk. The traditional method relies on fixed values and qualitative estimates, which are insensitive to intra-individual variance in physiological responses. This research introduces the Adaptive Fuzzy Logic-Based Training Load Management System (AFL-TLMS), an intelligent, real-time system for athlete monitoring and optimization that uses fuzzy-logic inference to dynamically adjust training loads. The AFL-TLMS employs Mamdani fuzzy inference systems and Gaussian membership functions to incorporate subjective (e.g., session rating of perceived exertion) and objective (e.g., heart rate variability, external load) markers. The system makes real-time adjustments to training advice based on athlete data, enabling individualized, adaptive load management. Validation on longitudinal athlete datasets demonstrated that AFL-TLMS enhanced training load classification accuracy by 15% and early injury risk detection by 20% compared to conventional threshold-based approaches. The study presents a new, interpretable, and adaptive sports science framework with real-world applications in athlete monitoring, real-time load optimization, and wearable device integration. AI-aided performance monitoring and real-time decision-making support will be explored in future research to further enhance load management practices.</p>