In order to enhance the prediction accuracy of the strip crown and improve the quality of final product in the hot strip rolling, an optimized model based upon support vector machine (SVM) is proposed firstly. Meanwhile, for purposes of enriching data information and ensuring data quality, the actual data from a hot-rolled plant are collected to establish prediction model, as well as the prediction performance of models was evaluated by using multiple indicators. Besides, the traditional SVM model and the combined prediction models with the particle swarm optimization (PSO) and the cuckoo search (CS) optimization algorithm are also proposed. Furthermore, the prediction performance comparisons of the three different methods are discussed and validated. The results show that the CS-SVM has the highest prediction accuracy compared to the other two methods, and the root mean squared error (RMSE) of the proposed CS-SVM is 2.05µm, and 98.11% of prediction data have an absolute error below 4.5μm. In addition, the results also demonstrated that the CS-SVM not only with faster convergence speed and higher prediction accuracy but can be well applied to the actual hot strip rolling production.