Abstract: The control of the final cooling temperature after medium plate rolling is a core technology for improving product quality and production efficiency. The accurate prediction and regulation of the temperature hit rate have attracted much attention from both the academic and industrial communities. Early research was dominated by mathematical analytical models, which constructed modified Newton′s cooling law models based on heat transfer theory. Although these models had high computational efficiency, their adaptability to complex working conditions was limited. With the development of computer technology, the finite difference method (FDM) and the finite element method (FEM) have been widely applied in temperature field simulation, enhancing prediction accuracy through discretization. However, they rely on a large amount of experimental data for parameter calibration and have high computational costs. In recent years, machine learning models, with their strong nonlinear mapping capabilities, have become a research hotspot. Algorithms such as BP neural networks and XGBoost models have shown significant advantages in predicting the final cooling temperature hit rate. For model optimization, scholars have proposed innovative methods such as adaptive tuning of hyperparameters. By integrating mechanism-based and data-driven strategies, these methods effectively address the bottlenecks of industrial data noise sensitivity and insufficient generalization ability.Although machine learning technologies have demonstrated significant application value in industrial production, the steel industry—as a quintessential traditional complex industrial sector—features production processes characterized by multi-stage coupling and high dynamism.This results in multiple adaptation challenges when introducing and implementing machine learning technologies, further constraining their large-scale application. Future research should focus on multi-modal coupling modeling, energy-saving cooling process optimization under low-carbon targets, and the application of explainable artificial intelligence in industrial decision-making, thereby promoting the leapfrog development of hot rolling cooling control towards intelligence and greenness.