The growing demand for battery-grade manganese compounds and the depletion of high-grade manganese resources have increased the importance of utilizing low-grade manganese ores. This study investigates the reductive leaching of a low-grade pyrolusite (MnO₂) ore from the Karaburun region of İzmir, Türkiye, through statistical optimization and process modeling. Prior to leaching, the ore was upgraded by high-intensity wet magnetic separation, increasing the manganese grade from 16.55% to 31.24% Mn while reducing the siliceous gangue content. Leaching parameters were optimized using Response Surface Methodology (RSM) based on a four-factor, three-level Face-Centered Central Composite Design (FCCCD). The effects of sulfuric acid concentration (0.5–4.0 M), hydrogen peroxide concentration (0.5–2.0 M), temperature (20–80°C), and reaction time (30–180 min) on manganese extraction were evaluated. A quadratic regression model was developed and validated by Analysis of Variance (ANOVA), showing high statistical significance (p < 0.0001), an excellent coefficient of determination (R² = 99.07%), and a non-significant lack-of-fit (p = 0.1124). Under optimum conditions, validation experiments achieved a manganese extraction efficiency of 95.86%, in close agreement with the predicted value of 96.12%. Unlike previous studies focusing solely on reductive leaching optimization, the present work integrates high-intensity wet magnetic separation with statistically optimized hydrometallurgical extraction, providing an efficient processing route for the utilization of low-grade pyrolusite resources. The proposed approach combines high manganese recovery with low operating temperature and rapid leaching kinetics, offering strong potential for sustainable industrial production of battery-grade manganese precursors.