The assessment of human posture and movement biomechanics is critical for maximizing athletic performance and implementing effective injury prevention protocols. Traditional assessment methods often suffer from subjectivity, high cost, and limited accessibility, hindering widespread routine screening. Artificial intelligence (AI), particularly via Computer Vision (CV) and Deep Learning (DL) technologies, represents a paradigm shift, enabling automated, objective, and quantifiable biomechanical analysis. AI models, utilizing hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architectures, can accurately calculate joint angles and model temporal movement patterns, providing real-time feedback crucial for dynamic sports and personalized rehabilitation. Validation studies demonstrate AI's clinical efficacy, with high reliability (e.g., Intra-class Correlation Coefficients up to 0.90 for lower-limb alignment) and strong correlation with radiographic gold standards (e.g., r > 0.70). In rehabilitation, AI systems have achieved predictive accuracies exceeding 97% in evaluating tailored exercise plans. Despite these advancements, significant challenges persist, including the critical need for increased dataset diversity, standardization of evaluation protocols, and addressing the fundamental ethical issues surrounding athlete data privacy, algorithmic transparency, and accountability within competitive sports contexts. Future progress lies in Explainable AI (XAI) and Digital Twin technology, promising to deliver interpretability and highly personalized predictive modeling.