The employment of Fermatean fuzzy soft sets with normalized distances in the perspective of diagnosis in medical treatment are observed in this research. The effectiveness of normalized distances—such as Fermatean, Hamming, and Euclidean measures—in handling the inherent uncertainty in medical data is evaluated. Through a comparative analysis, we assess their performance in medical diagnosis tasks, aiming to demonstrate how these methods can enhance the accuracy and reliability of diagnostic systems. The goal of the research is to shed light on how Fermatean fuzzy soft sets with normalized distances might enhance the precision and dependability of medical diagnostic systems. This illustrative example of Chikungunya high-lights the benefits of combining normalized distances into Fermatean fuzzy soft set frame-works, contributing to advancements in medical decision support systems.