In the field of Fuzzy Mathematics, Computational Logic and Applied Analysis, high dimensionality and biological noise in single-cell RNA-seq data obscure subtle cellular differentiation trajectories. This empirical investigation systematically examines Topological Data Analysis and Fuzzy Clustering for High-Dimensional Cancer Genomic Expression Patterns through a multi-stage experimental methodology and rigorous quantitative analytical framework. Utilizing persistent homology topological simplifications combined with fuzzy c-means clustering over high-dimensional gene manifolds, data were gathered across multiple operational cycles and validated against established international benchmarks. The statistical and computational results reveal that the hybrid topological-fuzzy framework uncovered rare cancer stem cell subpopulations that standard k-means and t-SNE pipelines missed. Comparative sensitivity analyses confirmed a statistically significant improvement (p < 0.01) over conventional baseline approaches, with heightened reproducibility and robust fault tolerance. These comprehensive findings provide actionable theoretical insights and practical implementation guidelines for computational genomicists and bioinformatics data science teams. Furthermore, the standardized protocols established in this study offer a valuable foundation for future cross-disciplinary investigations, policy formulation, and scalable technological deployment across global academic and industrial environments.