Evolving cybersecurity threats necessitates effective identification and mitigation measures. This literature review analyzes threat modeling, decision support systems, and machine learning (ML) in cybersecurity. This article analyzes the strengths and weaknesses of key frameworks including STRIDE, DREAD, and MITRE ATT&CK, with a focus on their practical use in various organizational situations. The evaluation divides the existing research into three categories: threat modeling for various systems, decision support tools to improve incident response, and unique ML algorithms for threat identification. Scholars are increasingly recognizing the importance of comprehensive threat modeling frameworks that incorporate Artificial Intelligence. The difficulties encountered in model validation, scalability, and real-time applicability highlight the need for future research focusing on hybrid techniques that combine the benefits of different frameworks with advanced ML algorithms. This synthesis serves as a platform for creating stronger cybersecurity solutions to protect against more sophisticated adversary threats.