In this paper, we explore the fermatean m-polar fuzzy max-min-max composite relation, improved fermatean m-polar fuzzy composite relation and propose refined fermatean m-polar fuzzy composite relation approach for better results. The validity of the refined fermatean m-polar fuzzy composite relation is tested through numerical experiments, comparing it to the traditional fermatean m-polar fuzzy max-min-max composite relation and improved fermatean m-polar fuzzy composite relation. The results demonstrate that the refined fermatean m-polar fuzzy composite relation provides superior output. In conclusion, the refined Fermatean m-polar fuzzy composite relation provides a powerful and flexible approach for handling uncertainty in real-life decision-making problems. By incorporating higher degrees of membership, non-membership, and hesitation, this method enhances accuracy and reliability in complex scenarios such as medical diagnosis, risk assessment, and multi-criteria decisionmaking. Its ability to process vague and imprecise information makes it a valuable tool for decision-makers, ensuring more informed and rational choices across various fields. Keywords: Fuzzy set, Fermatean m-Polar Fuzzy Set, Fermatean m-Polar Fuzzy max-min- max composite relation, improved fermatean m-polar fuzzy composite relation, refined fermatean m-polar fuzzy composite relation and applications.