• Random forest achieved the highest predictive accuracy (R² = 0.8541) for Zn recovery from carbonate ores in NaOH leaching.
• SHAP analysis identified NaOH concentration, leaching time, and solid-to-liquid ratio as the most positive influencers, while Ca, Fe, and Pb inhibited recovery.
• The study compiled 422 experimental observations and compared four regression models, offering a scalable framework for process optimization.
• Machine learning combined with explainable AI provides actionable insights for reagent optimization in hydrometallurgical zinc production.