As technology advances over the years, its complexity increases directly proportional. These advances bring new frontiers of performance, efficiency, and sustainability, and with that, new risks are identified in the production of these technologies. In aircraft engine maintenance activities, identifying and responding to risks is fundamental since an engine failure during a flight may cause a forced landing and, tragically, claim several lives. This reality makes it essential to monitor, identify, and prioritize risk treatment during aero-engine maintenance. What determines the essentiality and complexity of prioritizing risk treatment is the high number of risks identified - this is what usually happens in most repair stations. This fact was observed in a major aero-engine repair station located in South America. The authors conducted a literature review on probabilistic risk analysis in scientific databases associated with a case study at this repair station. A method to combine risks found in different ways in a single model to estimate the overall risk score of its operation is proposed. The objective is to embed the model into the Safety Management System to meet Civil Aviation Agency Regulations and the requirements AS9100. As a result, a Bayesian network modeling method integrated with Fuzzy Logic is proposed to combine risks generated from different sources. It assists in prioritizing decision-making in the treatment of operational risks in turbofan aero-engine maintenance, making more efficient the investment of resources in the treatment of identified risks and, consequently, quality continuous improvement and optimization of operational safety.