Ranking data plays a critical role in decision-making, preference modeling, and recommendation systems. Traditional ranking approaches often struggle to address the inherent uncertainty, subjectivity, and complexity present in real-world scenarios. This study explores the application of complex fuzzy sets (CFS) to enhance rank data generation and visualization, offering a robust solution to these challenges. Complex fuzzy sets extend classical fuzzy logic by incorporating phase angles, enabling more nuanced modeling of interdependent and ambiguous ranking criteria. We propose a novel methodology for rank data generation using CFS coupled with advanced visualization techniques for ranking comparison. The proposed approach addresses key challenges in ranking systems, including handling imprecise data, capturing multidimensional relationships, and enhancing interpretability. Through comparative analysis, we demonstrate that CFS-based ranking systems outperform traditional methods in their ability to model complex datasets and provide clearer insights. This research highlights the transformative potential of combining fuzzy logic with visualization techniques to improve the accuracy, transparency, and utility of ranking systems. The findings pave the way for future advancements in rank data modeling and decision support tools, offering significant implications for domains such as e-commerce, search engine optimization, and multicriteria decision analysis.