<![CDATA[Risk management is a strategic aspect that supports an organization’s sustainability amid increasing complexity, uncertainty, and the dynamics of the business environment. As various risk management approaches evolve, risk evaluation and risk prioritization have become two complementary stages in developing effective mitigation decisions. However, various studies indicate that these two processes are still often examined separately, thus failing to produce a comprehensive risk management framework. This study aims to identify research developments, the dominant methods used, research trends, the strengths and limitations of existing approaches, as well as research gaps related to the integration of risk evaluation and risk prioritization. The study employed a Systematic Literature Review (SLR) method in accordance with the PRISMA guidelines. The literature search was conducted using the Google Scholar, Scopus, and IEEE Xplore databases to identify articles published between 2015 and 2025. After undergoing the processes of identification, screening, eligibility assessment, and selection based on inclusion criteria, 25 articles were identified and analyzed in depth using a thematic analysis approach. The study’s findings indicate that the integration of risk evaluation and prioritization methods is increasingly evolving through the use of hybrid models—such as FMEA–AHP, FMEA–TOPSIS, FMEA–ANP, and the House of Risk—and is supported by the application of Artificial Intelligence, Machine Learning, Big Data Analytics, and Enterprise Risk Management. This integration has proven capable of enhancing the objectivity of assessments, the effectiveness of resource allocation, and the quality of decision-making in risk management. Nevertheless, existing research still faces several limitations, including a focus predominantly on large industrial sectors, a lack of empirical validation, limited attention to human behavioral factors, and the underutilization of dynamic models in small and medium-sized organizations. Therefore, future research needs to develop risk evaluation and prioritization models that are more adaptive, integrated, and empirically validated to address the challenges of an increasingly complex organizational environment.]]>
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