Employee performance evaluation constitutes a fundamental pillar of organizational effectiveness, especially in technical and service oriented sectors where human capital directly impacts operational reliability and customer satisfaction. Despite its strategic importance, conventional evaluation approaches rely largely on subjective judgments and often lack robustness in the face of uncertainty, incomplete information, and inconsistency in pairwise comparisons. To address these challenges, this study develops a Neutrosophic Sparse Analytic Hierarchy Process (NSAHP) framework that integrates Neutrosophic logic with dispersion regularization and enables simultaneous modeling of uncertainty, indeterminacy, and missing data. The proposed model is empirically applied to Tonekabon Electricity Distribution Company (TEDC) as a real world case study. The results show that through the systematic integration of expert judgments and the effective management of incomplete pairwise comparisons, the NSAHP framework generates stable, interpretable, and employee-friendly rankings. Comparative analyses against Fuzzy Analytic Hierarchy Process (FAHP) and Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) demonstrate the superiority of the proposed approach in terms of consistency, robustness to missing information, and computational efficiency. Experimental findings show a reduction in the average error of about 102 in five iterations, along with an approximate 35% improvement in computational performance compared to conventional fuzzy based models. Overall, this study contributes to the Multi Criteria Decision Making (MCDM) literature by demonstrating how advanced uncertainty aware methods can improve human resource evaluation systems and provide a reliable analytical basis for performance based managerial decision making.