In the field of Fuzzy Mathematics, Computational Logic and Applied Analysis, green manufacturing procurement requires evaluating complex supplier ESG performance under multi-dimensional decision hesitancy. This empirical investigation systematically examines Spherical Fuzzy TOPSIS Methodology for Sustainable Supply Chain Partner Selection in Green Manufacturing through a multi-stage experimental methodology and rigorous quantitative analytical framework. Utilizing spherical fuzzy sets with independent membership, non-membership, and hesitancy degrees applied in TOPSIS ranking algorithms, data were gathered across multiple operational cycles and validated against established international benchmarks. The statistical and computational results reveal that the spherical fuzzy TOPSIS method eliminated ranking reversal anomalies and provided robust supplier prioritization across forty manufacturing criteria. 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 green supply chain managers and sustainable procurement directors. 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.