In the field of Humanities, Social Sciences, Cultural Studies and Sociology, app-based platform gig work obscures gendered pay gaps, emotional labor burdens, and lack of social protections for female domestic workers. This empirical investigation systematically examines Gendered Labor Dynamics, Algorithmic Control, and Care Work in the Platform Gig Economy through a multi-stage experimental methodology and rigorous quantitative analytical framework. Utilizing intersectional feminist methodology combining app usage logs, digital diaries, and structural labor market analysis of 240 gig workers, data were gathered across multiple operational cycles and validated against established international benchmarks. The statistical and computational results reveal that algorithmic rating systems penalized female workers for care responsibilities, entrenching precarity behind flexible platform rhetoric. 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 labor economists, gender studies scholars, and social policy regulatory agencies. 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.