This study explores the potential of utilizing serious games as an innovative approach to diagnose depression by analyzing user behavior within a gaming environment. Traditional methods of depression diagnosis rely heavily on standardized questionnaires and clinical assessments, which can be subjective and prone to inaccuracies. Leveraging advances in information technology and machine learning, this research proposes a novel methodology where gamified environments provide controlled conditions for observing and evaluating user behaviors indicative of depressive symptoms. Key features of the study include the design of an interactive game scenario tailored to elicit behaviors relevant to depression diagnosis, the collection of objective indicators such as reaction times and number of actions, and the subsequent application of classification algorithms to distinguish between individuals with and without confirmed depression diagnoses. Preliminary findings suggest promising results, particularly with Support Vector Machines and Random Forests demonstrating higher accuracy in identifying depressive patterns. Future improvements could enhance the method’s robustness by incorporating vocal and facial analyses, expanding the scope of the game narrative, and refining the underlying predictive models. Ultimately, this study highlights the potential of gamification techniques in enhancing early detection and intervention efforts for depression.