Abstract: The viscosity of refining slags plays a critical role in metallurgical processes. However, obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments, often relying on empirical models with limited predictive capabilities. This study focuses on the influence of optical basicity on viscosity in CaO–Al₂O₃-based refining slags, leveraging machine learning to address data scarcity and improve prediction accuracy. An automated framework for algorithm integration, parameter tuning, and evaluation ranking framework (Auto-APE) is employed to develop customized data-driven models for various slag systems, including CaO–Al₂O₃–SiO₂, CaO–Al₂O₃–CaF₂, CaO–Al₂O₃–SiO₂–MgO, and CaO–Al₂O₃–SiO₂–MgO–CaF₂. By incorporating optical basicity as a key feature, the models achieve an average validation error of 8.0% to 15.1%, significantly outperforming traditional empirical models. Additionally, symbolic regression is introduced to rapidly construct domain-specific features, such as optical basicity-like descriptors, offering a potential breakthrough in performance prediction for small datasets. This work highlights the critical role of domain-specific knowledge in understanding and predicting viscosity, providing a robust machine learning-based approach for optimizing refining slag properties.