Abstract: The slag grinding process is essential for manufacturing slag powder, which is a green raw material. The quality of its indicators directly affects the economy and safety of the entire process. However, this process involves multiple controlled variables and features strong nonlinearity, large feedback delays, and frequent fluctuations in operating conditions, making it difficult to control the indicators of quality, throughput, and temperature. To address these issues, this paper designs an intelligent control system for slag grinding based on model predictive control. The system first performs intelligent optimization of the control targets through an existing platform, and then controls the quality, throughput, and temperature indicators using the model predictive control method. Practical operating results show that the application of this system reduces the unit gas consumption and unit power consumption from 35.7 m³/t and 40.4 kWh/t to 34.1 m³/t and 38.7 kWh/t, respectively. These results indicate that the proposed method can significantly reduce energy consumption while maintaining stable product quality, thus demonstrating its practicality and effectiveness.