Early detection of mental health disorders is a major challenge in the field of health. The forward chaining method and the Dempster-Shafer theory are two approaches that can be used in the development of expert systems for early detection of mental health disorders. The forward chaining method is used to identify early symptoms that may indicate mental health disturbances, whereas the dampster-sshafer theories are used to manage uncertainty in the conclusion process, while the dampster-shafer theorem is used for managing uncertainties in the diagnosis process. The study combines both approaches in the development of an expert system for the early detection of mental health disorders. Furthermore, Dempster-Shafer's theory is used to combine evidence from various symptoms and take into account the uncertainty in diagnosis. This method is implemented in a computer-based expert system that can assist health professionals in the early detection of mental health disorders in patients. The system tests were conducted using information from a number of patients who had been clinically diagnosed, and the results suggested that this approach could provide accurate results in the early detection of mental health disorders. In conclusion, the combination of the forward chaining method and the Dempster-Shafer theory achieved 100% accuracy of the system with an average density of 73,496%.