In the field of Fuzzy Mathematics, Computational Logic and Applied Analysis, complex multi-symptom medical diagnostic decisions involve hesitant and non-standard membership degree trade-offs. This empirical investigation systematically examines Pythagorean Fuzzy Soft Sets and Their Applications in Medical Diagnostic Decision Support Algorithms through a multi-stage experimental methodology and rigorous quantitative analytical framework. Utilizing Pythagorean fuzzy soft aggregation operators and distance metric formulations evaluated on clinical cardiology patient case databases, data were gathered across multiple operational cycles and validated against established international benchmarks. The statistical and computational results reveal that the proposed aggregation algorithm resolved diagnostic ambiguity in 94% of multi-morbidity patient cases with high consistency. 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 clinical decision support system developers and medical biostatisticians. 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.