Musculoskeletal disorders (MSDs) are a significant concern in occupational health, affecting workers across various industries and work environments. Effective recognition and management of MSDs are essential to improving workplace ergonomics and safeguarding workers’ health. This study conducts a comprehensive review of existing research to compare and evaluate different machine learning (ML) algorithms for human posture identification, focusing on key performance metrics such as precision, speed, and relevance. It examines Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and hybrid approaches that integrate multiple models. In addition to assessing their classification performance, the study highlights key challenges, including data dependency and the risk of overfitting. The findings indicate that both CNNs and ANNs achieve high classification accuracy for posture recognition, with hybrid models demonstrating even greater reliability. Furthermore, the evaluation considers the impact of various sensor technologies on system performance, emphasizing the necessity of incorporating high-precision sensors for improved accuracy. This review provides an analysis of the current state of ML-based posture detection models and their potential to enhance MSD prevention. By advancing posture detection technology, these insights contribute to the development of more effective solutions for mitigating MSD risks and promoting healthier workplace environments.