Accurate biomechanical modelling of human gait plays a crucial role in clinical diagnostics, rehabilitation, prosthesis design, and sports biomechanics. Existing motion capture systems, particularly marker less or IMU-based methods, often face challenges related to accuracy, soft tissue artifacts, real-time computation, and limited generalizability. This study proposes an advanced biomechanical gait modeling framework that integrates high-precision marker-based motion capture with computational analysis, sensor fusion, and real-time musculoskeletal dynamics estimation. The proposed system utilizes a hybrid approach combining optical motion capture, inertial sensors, and force plate data. Soft tissue artefact compensation algorithms and sensor drift correction techniques are applied to improve data fidelity. Real-time inverse dynamics and musculoskeletal modeling are performed to estimate joint torques, ground reaction forces, and muscle efforts. Validation is conducted on a diverse subject pool across multiple gait conditions, including clinical populations. The integrated framework demonstrates superior accuracy in joint kinematics and kinetics compared to conventional systems. Real-time processing capabilities enable adaptive feedback for clinical and sports applications. The explainability module enhances clinical trust by providing interpretable biomechanical parameters and movement quality indices. The proposed high-precision biomechanical modeling approach successfully overcomes key limitations of existing gait analysis systems. Its real-time capability, multi-sensor integration, and clinical applicability offer significant advancements for both research and clinical practice.