This study examines the impact of the number of experts and evaluation scales on criteria weighting in a Multi-Criteria Decision-Making (MCDM) environment. A hybrid approach integrating fuzzy Analytic Hierarchy Process (AHP) with bootstrapping is employed to analyze the variation of criteria weights as the number of experts increases. Fuzzy AHP is utilized to handle the inherent uncertainty in expert judgments, while bootstrapping enhances result reliability through iterative resampling. The study evaluates the effect of different linguistic evaluation scales and varying numbers of criteria using data from 50 expert evaluations. The findings indicate that criteria weights tend to stabilize beyond 20 experts, and higher sensitivity in linguistic scales has a minimal effect on weight variation. Additionally, the consistency ratio of aggregated matrices improves with an increasing number of experts, though convergence is achieved faster when using a less sensitive linguistic scale. These insights contribute to refining decision-support frameworks by addressing subjectivity in expert-driven MCDM processes.