The rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), and advanced sports analytics has fundamentally transformed athlete monitoring and performance optimization in contemporary competitive sports. Given the extreme physiological and biomechanical demands of wrestling which requires exceptional levels of maximal strength, explosive power, muscular endurance, and neuromuscular coordination accurate and continuous performance evaluation is absolutely critical for achieving competitive excellence. Historically, strength assessment in combat sports has relied on traditional methods such as one-repetition maximum (1RM) testing, subjective coach observations, and periodic fitness evaluations. However, these conventional approaches are inherently limited; they provide only intermittent, snapshot data and often fail to capture real-time physiological fluctuations, cumulative training adaptations, or the early, subtle indicators of overtraining and fatigue. To overcome these limitations, modern sports science has increasingly integrated AI-assisted monitoring with highly sophisticated equipment, including wearable biomechanical sensors, Inertial Measurement Units (IMUs), force platforms, and heart rate variability (HRV) devices. These tools continuously capture vast, multi-dimensional biomechanical and physiological datasets during both training sessions and competitive matches. Machine learning algorithms such as Artificial Neural Networks (ANN), Random Forest Models, Support Vector Machines (SVM), and Deep Learning architectures subsequently process this influx of data to identify complex hidden patterns, predict athletic outcomes, assess training effectiveness, and proactively detect potential injury risks before they manifest into severe physical setbacks.