Human resources are considered as one of the most valuable assets of an organization. Therefore, the top priorities of organizational managers include paying attention to manpower and providing necessary facilities to bring about satisfaction with workplace and working conditions in the light of mental and physical health. Accordingly, mental health has partly been neglected by managers because it lacks concrete and tangible aspects on the contrary to other organizational dimensions such as buildings and machinery. This study was conducted to achieve the fundamental goal of identifying the aspects and characteristics affecting the mental health of employees at technology-based companies as well as the factors resulting in mental problems and disorders. It was also decided to develop a model to predict affliction or non-affliction with mental problems among employees in the long term. For this purpose, this study focused on data mining techniques in order to identify latent relationships and introduce a prediction model. Therefore, the important and major factors were identified as individual characteristics, occupational characteristics, organizational performance features, and organizational governing culture features in mental health. The analysis and evaluation of results indicated that Classification algorithms managed to predict the mental health of employees with nearly %85 of accuracy. Besides, Clustering algorithms succeeded in dividing the samples into high-risk, medium-risk, and low-risk classes. The designed models can help organizational managers identify the factors affecting the mental health of employees and predict the chances of affliction with mental health disorders to prevent destructive harm to employees and organizations.