<p>In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel, a predictive modeling method named RFR-WOA is developed based on random forest regression (RFR) and whale optimization algorithm (WOA). Firstly, using Pearson and Spearman correlation analysis and Gini coefficient importance ranking on an actual production dataset containing 37,878 samples, 22 key variables are selected as model inputs from 112 variables that affect mechanical properties. Subsequently, an RFR-based predictive model for the mechanical properties of cold-rolled strip steel is constructed. Then, with the combination of the coefficient of determination (<i>R</i>²) and root mean square error as the optimization objective, the hyperparameters of RFR model are iteratively optimized using WOA, and better predictive effectiveness is obtained. Finally, the mechanical properties prediction model based on RFR-WOA is compared with models established using deep neural networks, convolutional neural networks, and other methods. The test results on 9469 samples of actual production data show that the model developed present has better predictive accuracy and generalization capability.</p>