In this study, a comprehensive Fuzzy Logic-based evaluation model is developed to assess institutional performance in academic settings by integrating subjective feedback from multiple stakeholders. Traditional evaluation methods often fall short in capturing the complexity and ambiguity inherent in qualitative assessments provided by managers, academic staff, and alumni. To address this limitation, the proposed model utilizes the MATLAB Fuzzy Toolbox to design a rule-based inference system that incorporates three input variables: Managers’ Evaluation (ME), Academic Staff Members’ Evaluation (ASE), and Alumni Evaluation (AE). Each input is defined using linguistic terms and corresponding membership functions, enabling the transformation of vague human judgments into structured computational outputs. The output variable—Institutional Performance Level—is inferred through a set of fuzzy rules that reflect the nonlinear relationships among inputs. Visualization tools such as Rule Viewer and Surface Viewer are used to enhance interpretability and transparency of the decision-making process. The findings demonstrate the effectiveness of fuzzy logic in modeling complex academic evaluation systems, supporting more adaptive, inclusive, and nuanced assessments.