This paper presents a novel approach to real-time squat pose assessment and injury risk prediction using an enhanced temporal convolutional neural network (TCNN) architecture. The proposed system integrates multi-stream feature extraction with biomechanical constraints to achieve accurate movement analysis while maintaining real-time performance. The architecture implements parallel processing pathways optimized for spatial-temporal feature extraction, achieving a 37.8% reduction in processing latency compared to existing methods. A comprehensive dataset of 15,000 squat movement sequences from 500 participants was used to train and validate the system. The enhanced TCNN incorporates adaptive attention mechanisms and biomechanical constraints, resulting in a 44.8% decrease in mean angular error and a 27.3% improvement in physiological validity. Experimental results demonstrate real-time performance at 35.7 FPS with 94.7% assessment accuracy across diverse movement patterns. The system maintains sub-30ms latency while providing comprehensive movement analysis and risk assessment capabilities. The integration of constraint enforcement mechanisms ensures anatomically plausible pose estimates without compromising computational efficiency. The proposed approach advances the state-of-the-art in automated movement assessment, establishing a new benchmark for real-time biomechanical analysis systems in athletic training and rehabilitation applications.