The petroleum industry has an important impact on the environment, which is why risk analysis for a petroleum storage tank is required in the process industry. However, conducting an exhaustive study is frequently complicated due to the incomplete data based on historical system information, feedback, or test results. The main goal of this study is to develop an innovative framework that integrates the Bow-tie method with MIMAH (Methodology for the Identification of Major Accident Hazards). This approach specifically aims to identify the most critical accident scenario and comprehensively list all realistic causes (basic events) associated with petroleum storage tanks. Acquiring historical data on basic events poses challenges due to the lack, insufficiency, and imprecision of failure data. To address this obstacle and predict the system’s evolution, a combination of established methods for handling uncertainty and multi-criteria decision-making has been proposed. This combination incorporates a modified Fuzzy Analytical Hierarchy Process with Chang’s Extent Analysis Method (FAHP-EAM), fuzzy set theory, and expert elicitation, contributing to an extensive approach for managing uncertainty and making informed decisions in the absence of comprehensive historical data. Furthermore, the Bow-tie model is converted into a dynamic Bayesian network to improve risk analysis by integrating probabilistic reasoning and capturing the dynamic interactions among multiple risk factors. This integrated approach is validated on real scenarios. The utilization of dynamic Bayesian networks further facilitates the suggestion of scenarios to assess the contribution of basic events to the critical event and evaluate the resulting environmental impacts. Finally, the prediction of threat zones for various hazardous effects, such as toxicity, thermal radiation, and overpressure linked to chemical releases following the occurrence of the critical event, is carried out. The risk analysis outcomes from this study offer valuable data for assessment, enabling managers to improve the quality of risk management and formulate effective risk mitigation strategies.