Abstarct: The mining industry, as one of the pillars of the national economy, plays an important role in providing raw materials for industries and creating added value from natural resources. In the meantime, optimal management of low-grade reserves and optimization of the mineral blending process are strategic challenges in mining operations. Angouran Lead and Zinc Mine, the largest supplier of zinc ore in Iran, is faced with the accumulation of a significant volume of low-grade sulfur ore in its depots. Despite the significant extraction volume, this mineral has had very low sales in recent years compared to other depots. The main objective of this research is to present an optimal blending plan to convert this mineral into an economically viable product that meets the needs of processing plants. For this purpose, comprehensive data on physical and chemical characteristics, depot volumes, production costs, and market prices were first collected. Then, a mixed integer linear programming mathematical model was developed that simultaneously maximized net profit and ensured maximum utilization of the low-grade sulfur depot. The incompatibility between strategic, qualitative, and economic constraints made the developed model unfeasible. Then, a supplementary model was designed by introducing deviation variables and a penalty objective function that was able to achieve an operational solution without violating the constraints. The results showed that by observing the maximum permissible zinc sulfide grade, the entire inventory of the low-grade sulfur depot was consumed in the optimal combination, and a profit equivalent to 319.5 trillion rials was obtained. Sensitivity analysis showed that increasing market flexibility beyond 9% would not lead to an increase in profit due to the depletion of the inventory of profitable depots. To assess the stability of the solution, sensitivity analysis on effective parameters and Monte Carlo simulation were performed to measure the risk arising from grade uncertainty. The results confirmed that the proposed solution has high sustainability and very low financial risk. In addition to providing an operational and applicable solution, this research has provided a decision-making frxamework for intelligent management of mineral resources in unfavorable conditions.