Slag foaming is a key phenomenon in electric arc furnace (EAF) steelmaking, which improves thermal efficiency, suppressing metal splashing, and stabilizing the refining process. Accurate prediction and control of slag foaming are essential for achieving green and efficient steelmaking. This review aims to provide a systematic overview of the research progress on slag foaming prediction, clarifying the applicability, advantages, and limitations of different predictive methods to support intelligent control of foamy slags. Following the framework of "influencing factors-prediction methods-development trends", this review summarizes the coupling effects of multiple variables such as basicity, viscosity, surface tension, suspended particles, gas parameters, and temperature on foam formation and stability. Furthermore, it compares five major prediction approaches, including empirical formulas, dimensionless modeling, thermodynamic calculations, computational fluid dynamics (CFD) simulations, and machine learning models, and analyzes their core concepts, merits, and constraints. The results indicate that single models often struggle to balance real-time capability and accuracy, particularly under multi-variable coupling and complex operating conditions. Therefore, a hybrid prediction framework combining mechanism-based and data-driven models is proposed, emphasizing the importance of physical constraints, multi-scale coupling, and multi-source data fusion. This integrated approach is expected to advance slag foaming prediction from "computable" to "controllable and adjustable", offering methodological insights for the development of green and intelligent EAF steelmaking.