Abstract: A machine learning based method for predicting the amount of molten iron in blast furnaces was proposed to address the importance of predicting the amount of molten iron in ladle scheduling and improving production efficiency, as well as the problem of insufficient prediction accuracy of traditional mechanism models. Based on the blast furnace production data of a certain steel plant from January 2024 to March 2025, a high-quality dataset was constructed through missing value filling, outlier processing, and Z-Score standardization. Pearson correlation analysis and random forest feature importance evaluation were combined to screen 19 key parameters such as soft water pressure, gas utilization rate, and coal quantity. The performance of AdaBoost, random forest, support vector machine, neural network, and linear regression models were compared. The results showed that the AdaBoost model performed the best in predicting the amount of molten iron, with a fitting goodness of R2 of 0.78 and a Mean Square Error (MSE) of 13.59. The prediction accuracy reached 87.2% within the range of ±10 tons and 94.1% within the range of ±15 tons. The model using the Stacking integrated framework achieved a prediction accuracy of 100% within the range of ±15 tons, which can effectively support actual production scheduling needs. This method provides a feasible data-driven solution for accurate prediction of the amount of molten iron in blast furnaces.