This research was conducted with the aim of developing and presenting a model for preventing toxic employee behaviors in organizations (case study: Iranian insurance industry). The present research was mixed in terms of methodology (qualitative-quantitative) and was implemented in two stages. In the qualitative part, in order to collect data, semi-structured interviews were conducted with experts and the resulting data were examined using the content analysis method. In this stage, by using the coding, summarization, interpretation of concepts and extraction of themes, the components and dimensions effective in preventing toxic employee behaviors were identified. Qualitative data analysis was performed using MAXQDA software. The participants in this part included 12 experts in the fields of human resource management, organizational behavior and the insurance industry who were selected using the purposive sampling method and in-depth and semi-structured interviews were conducted with them. In the second stage of the research, in order to determine the relationships between the extracted components and the ranking of the effective factors, the structural-interpretive modeling (ISM) method was used. In this stage, the required data were collected from experts through the ISM questionnaire and a researcher-made questionnaire with a five-point Likert scale. Then, the relationships between the components, the hierarchical levels of the factors, and their influence and dependence were analyzed using the structural-interpretive modeling method and MICMAC software. The results of the structural-interpretive ranking showed that the prevention of toxic employee behaviors is influenced by organizational, managerial, and behavioral factors. At levels close to the final outcome, healthy organizational communications, conflict management, a climate of trust, and psychological safety play the greatest role in reducing toxic behaviors. Also, workplace mental health, organizational justice, and employee empowerment, as mediating factors, are influenced by human resource policies and behavioral monitoring systems.