<p>Distances are used to depict the difference between two fuzzy sets in fuzzy logic, providing enhanced accuracy in decision making compared to other fuzzy techniques. These distances play a vital role in managing complex decision-making issues in our daily lives. This paper introduces a novel distance measure in the context of fuzzy sets, specifically the “Manhattan distance for Intuitionistic Fuzzy Sets (IFSs),” to evaluate the degree of dissimilarity between two IFSs. This measure is compared with widely used distance measures such as the Hamming distance, Euclidean distance, Minkowski distance, and many more. Examples from pattern recognition and multi-criteria decision making (MCDM) are used to demonstrate its effectiveness. An extensive comparative analysis of the proposed measure with existing measures in pattern recognition and MCDM further validates the proposed measure. The numerical results highlight the superior performance and broad applicability of the proposed method compared to existing approaches.</p>