The temperature and silicon content of molten blast furnace iron are directly related to its quality. Therefore, creating an effective prediction model for these parameters is crucial. To address these issues, an Improved Arithmetic Optimization Twin Support Vector Machine for Regression (LAOA-TSVR) model was developed to predict the temperature and silicon content of molten blast furnace iron. First, SPSS was used to perform a correlation analysis and identify the main influencing factors. Secondly, the model was compared with three common prediction models to verify its prediction performance: Back Propagation Neural Network (BP), Support Vector Regression (SVR), and Twin Support Vector Machine for Regression (TSVR). Preliminary results indicate that the prediction accuracy of the LAOA-TSVR model is significantly higher than that of the other models. Finally, the model was applied to the actual production process of an iron mill for a total of 200 furnaces. The results show that the hit rates for molten iron temperature and silicon content are within the error ranges of ?5% and ?0.5% at 92.12% and 92.53%, respectively, with a corresponding double-hit rate of 85.32%. The model effectively fulfills the production requirements of an iron mill and provides valuable information for the production process in the blast furnace.