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Integrating computer vision and machine learning with observation-based ergonomic posture risk assessment methods represents a significant advancement in occupational health and safety, which is essential in reducing work-related musculoskeletal disorders. This collaboration has enhanced the efficiency and scope of ergonomic posture risk assessments. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline was utilized to select relevant papers in this review. A total of 583 research articles were retrieved from the Web of Science and IEEE Xplore databases, and 30 articles meeting the screening criteria were selected for detailed analysis. The results were organized into three distinct stages: Data Preparation, Pose Estimation, and Risk Assessment. During the Data Preparation Stage, the data acquisition devices, datasets, experimental conditions, and observation-based ergonomic posture risk assessment tools utilized in each article were discussed. Subsequently, in the Pose Estimation Stage, the human pose estimation techniques employed and their ability to detect wrist details were outlined. Finally, the risk assessment methods were presented, encompassing Directly Assess Risk Levels, Predict Risk Levels Using Traditional Machine Learning Algorithms, and Advanced Risk Prediction Using Deep Learning Algorithms. We propose several directions for future research. By synthesizing the current literature and identifying critical areas for future development, this review aims to provide a comprehensive overview of the intersection between computer vision, machine learning, and observational ergonomic posture risk assessment, with implications for improving occupational health and safety.