Fuzzy logic provides a method to transform subjective risk assessments into a structured and quantifiable decision-making process. This process includes fuzzifying inputs, applying fuzzy rules, aggregating results, and defuzzifying the output to yield a crisp risk score. By managing uncertainty and imprecision, fuzzy logic proves to be a powerful tool in risk assessment. The Mamdani inference method, in particular, offers a systematic approach to combining fuzzy rules and generating a fuzzy output, which is then defuzzified into a precise risk value. This approach effectively addresses the complexities and uncertainties in risk assessment, enhancing decision-making capabilities. Fuzzy logic, distinct from classical binary logic, offers a framework for reasoning that accommodates approximation rather than fixed, exact values. An FIS involves fuzzification, rule evaluation, aggregation of outputs, and defuzzification to convert fuzzy inputs to crisp outputs. Different types of FIS, like the Mamdani FIS, employ fuzzy sets in their operations to manage and interpret imprecise data effectively