International Research Journal of Education and Technology

Depression and mental health issues are increasingly prevalent among IT workers due to high workloads, long screen time, tight deadlines, and job insecurity. Continuous stress and sedentary work environments significantly impact emotional well-being and productivity. This project proposes a Depression and Mental Health Predictor system specifically designed for IT professionals. The system uses machine learning algorithms to analyze factors such as working hours, sleep patterns, stress levels, job satisfaction, and social interaction. Data is collected through structured questionnaires and digital activity tracking. The model processes the input data to identify early signs of depression and mental health risks. Predictive analytics helps in classifying employees into different risk levels. The system provides personalized recommendations such as stress management techniques and professional support guidance. Early detection can reduce severe psychological consequences and improve workplace efficiency. The model ensures data privacy and secure handling of sensitive information. This solution supports organizations in promoting employee well-being and preventive mental healthcare. The proposed system aims to create a healthier work environment in the IT sector. By leveraging artificial intelligence, the project contributes to proactive mental health monitoring. Overall, this predictor enhances awareness, productivity, and quality of life among IT workers.