Musculoskeletal issues ranges from pains to mobility issues. Timely diagnosis of musculoskeletal disorders are important in prevention and treatment. Anal- ysis of ergonomics of consumer durables fitting to human are always a complex task associated with time consuming and laborious work. In this present work, a machine learning based ergonomic analysis on knee with external support brace was carried out to understand the user comport of basic postures among a reference healthy and affected population. The study involves 3D scanning of knee joint of study population grouped in to different age groups and each group containing both healthy reference and population with prevalence of knee joint issues. The disease prevalence was categorized into low, medium and extreme cases based on the severity of mobility issues diagnosed. Duration of present in a particular posture is also recorded. Piezoelectric based pres- sure measurements carried out at important pressure contacts of the user. The data were feed in to MATLAB machine learning tool to train the system, after obtaining optimum training outcome testing and validation were performed to obtain the performance characteristics in terms of ergonomic comfort. The proposed system was able to classify between different comfort levels and may be evaluated with larger dataset for further clinical usage.