Risk analysis is playing a critical role for industries to evaluate and observe the incident/accidents. In today’s competitive world industries are focusing on the best solution to evaluate the risk which may leads industry to loss their reputation. This research investigates the application of Mamdani and Sugeno Fuzzy Inference methodologies, incorporating various membership functions, for risk assessment within the oil and gas industry’s case studies. The study emphasizes the economic significance of this sector, recognizing its essential role in supporting transportation, manufacturing, and energy sectors, while also addressing potential threats from hazardous facilities. Through a comprehensive Fuzzy Logic-based risk assessment model, with a focus on Mamdani and Sugeno interface systems, the research explores the qualitative and quantitative aspects of risk evaluation. The results indicate the consistent superiority of the Fuzzy Mamdani approach in terms of accuracy and efficiency over the Sugeno method. The model, comprising Likelihood and Consequence input variables and a Total Risk output variable, is evaluated through both qualitative and quantitative analyses. Whereas, the outcomes are based on different membership functions and showcases three case studies, each considering four key factors.