The mining industry is known to be one of the most hazardous working environments worldwide. Therefore, assessing and mitigating the risks associated with mining operations is essential for ensuring the safety of workers. Traditional methods of risk assessment in mines are usually deterministic. Further conventional methods do not consider the relative importance of the various experts, which often results in underestimating or overestimating the risks associated with a given hazard. To address the issue, a novel approach has been proposed for assessing risks associated with various hazards in mines. This approach leverages the fuzzy opinion aggregation method within a framework of group decision-making and fuzzy inference system. By utilizing the fuzzy opinion aggregation method, the subjective judgments of multiple experts can be consolidated into a single consensus opinion, considering their relative importance. The fuzzy inference system provides a robust framework for reasoning and making decisions in the presence of uncertainty. Experts were consulted and asked to provide their opinions on the likelihood and severity of hazards using fuzzy linguistic terms. The Similarity Aggregation Method (SAM) was employed to generate fuzzy numbers representing the likelihood and severity of each hazard. These fuzzy numbers were then defuzzified into crisp values. Furthermore, these crisp values of likelihood and severity was fed into rule base fuzzy inference system to obtain risk score. Based on the assessment, hazards such as rolling stones related hazards, fires in stockyards, dumper collisions, unauthorized personnel presence, dump failures, and uncontrolled movement of heavy earth-moving machinery were identified as having the highest risk scores. The proposed method is expected to improve the accuracy and reliability of risk assessment. By integrating the opinions of all team members, this method reduces the subjectivity and bias. Furthermore, by using fuzzy logic to model uncertainties, the method can account for incomplete or imprecise information, resulting in more realistic and accurate risk assessments.