In today's intricate decision-making field, characterized by diverse variables and conflicting objectives, the demand for reliable, knowledge-driven approaches is crucial. Multi-Criteria Decision Analysis (MCDA) methods serve as relevant tools in this area, offering structured evaluation frameworks to guide comprehensive decision support processes. However, the variety of MCDA methods introduces a significant challenge. These methods often provide divergent rankings, necessitating compromise solution methods to include those differences and propose cohesive recommendations. While established techniques address this need, they often overlook the potential synergy between positional rankings and preference scores, limiting the depth of insights provided. To fill this gap, this study introduces a novel Compromise Fuzzy Ranking (CFR) method, which integrates positional rankings and preference scores to offer nuanced and comprehensive consensus in multi-criteria evaluations. CFR's efficacy is demonstrated through simulation and theoretical examples, highlighting its potential to enhance decision support systems and navigate the complexities of contemporary decision-making challenges.