The by-product gases generated during steel manufacturing processes, including blast furnace gas, coke oven gas, and Linz–Donawitz gas, exhibit considerable variability in composition and supply. Consequently, achieving stable combustion control of these gases is critical for improving boiler efficiency. This study developed the advanced boiler combustion control model (ABCCM) by combining the random forest (RF) and classification and regression tree (CART) algorithms to optimize the combustion of steam power boilers using steel by-product gases. The ABCCM derives optimal combustion patterns in real time using the RF algorithm and minimizes fuel consumption through the CART algorithm, thereby optimizing the overall gross heat rate. The results demonstrate that the ABCCM achieves a 0.86% improvement in combustion efficiency and a 1.7% increase in power generation efficiency compared to manual control methods. Moreover, the model reduces the gross heat rate by 58.3 kcal/kWh, which translates into an estimated annual energy cost saving of USD 89.6 K. These improvements contribute considerably to reducing carbon emissions, with the ABCCM being able to optimize fuel utilization and minimize excess air supply, thus enhancing the overall sustainability of steelmaking operations. This study underscores the potential of the ABCCM to extend beyond the steel industry.