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This study introduces a novel approach named the fuzzy Kemeny median indicator ranks accordance (KEMIRA) method tailored for multiattribute decision making (MADM) while capturing and processing the uncertainties inherent in complex problems. We explore preferential voting to enhance MADM models, rewriting it as a linear programming (LP) problem with weight restrictions. Our fuzzy KEMIRA model leverages LP to ascertain optimal priorities and weights for each feature, guided by discrimination intensity functions. To illustrate the effectiveness of our approach, we utilize a well-known numerical example from the literature. We also present a case study describing the location selection of an innovation park constrained by experts’ subjective judgments across various attributes. Through comparative analyses with hesitant fuzzy KEMIRA and stochastic KEMIRA, we demonstrate our proposed fuzzy KEMIRA method's higher flexibility and reduced computational burden. By emphasizing these attributes, we underscore the versatility of our method, which applies to a broad spectrum of MADM problems that go well beyond specific instances.