Similarity measures are crucial for identifying distinctions between various alternatives. It is frequently represented by a number between 0 and 1, where 0 denotes poor similarity while 1 means high similarity. They are used to select the best alternative available over the other; mainly when the alternatives involve vague or blurry information. In the present work, a novel similarity measure utilizing the well-known Hausdorff metric is put forth for the neutrosophic soft set. The efficacy of the proposed measure lies in its simplicity in implementation over the existing measures available in the literature for neutrosophic soft sets. The utility of the suggested measure in decision-making is demonstrated through a medical diagnosis problem that confirms its reliability and applicability in real life scenarios.