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The scope of this research is the prediction of a cast billet surface temperature, which it will have in the rolling mill after the heating process. The main problem is that such a prediction is needed before the cast billet will really leave the furnace. In many cases, the boundary value problem of the heat transfer, particularly the differential equations of the transient heat conduction, is used to solve this problem. But in this research an alternative data-driven approach is proposed, which is based on a model of the dependence of the billet temperature on the retrospection of its heating in the continuous furnace. Such a model is developed as a result of the analysis of the data from the furnace control system. Such data from the real furnace were collected and stored in the data warehouse. Their exploratory analysis was conducted. All data were splitted into training, testing and validation subsets. As a part of this research, the regression model previously developed by the authors was also validated. It seemed to be overfitted (the error on the test set was significantly higher than the one on the training set). To overcome this disadvantage, an alternative method to develop the required data-based model is proposed by authors on the basis of the Boosting and Bagging algorithms. They belong to the machine learning field. As a result of the experiments with the bagging and boosting, the required model structure was chosen as a “Random Forest” with special class of the regression trees known as DART (Dropout Adaptive Regression Trees). Based on a significant number of experiments with that model, the two confidence intervals of the temperature prediction were found: 68 % and 95 % ones. The mean value of the temperature prediction error was estimated as ~ 9 °C for both the test and validation sets.