Musculoskeletal diseases can be dependent on numerous environmental and individual factors. The existing methods leave out several important factors during the calculation of risks. Hence, this paper has the purpose to create and examine a way for employees to estimate their own risk of developing musculoskeletal problems employing the Musculoskeletal Disease Risk Factor Assessment (MDRFA) approach. Exactly 112 male cement manufacturing workers participated in this cross-sectional investigation. Participants were interviewed to acquire data about their own possessions. Information on the objects was gleaned through perceiving their work and talking to them. Additionally, they were given the Cornell questionnaires for musculoskeletal pain and instructed to complete them out in Persian (CMDQ). A model was developed using Structural Equation Modeling (SEM) effect coefficients. This technique was verified using a linear regression analysis, and the final score was categorized using the Receiver Operating Characteristic (ROC). The musculoskeletal symptoms are seen to be considerably influenced by either the individual's own characteristics (total coefficient of 0.27) or by physical (total coefficient of 0.51) and psychological (total coefficient of 0.11). Items' computed coefficients were utilized to formulate the MDRFA equation. The ideal cut-off values for the final score of the approach were 14.32, 18.56, and 22.59, creating four distinct categories. The MDRFA approach could adequately explain 73% of risks, but the Rapid Entire Body Assessment (REBA) approach could only justify 51% of risks. Prediction of musculoskeletal disorders relies heavily on personal, physical and psychological characteristics. This methodology may allow for more precise prediction of the occurrence of certain diseases.