To address the issue of low precision in rolled thickness prediction for titanium/steel‐clad plates, theoretical calculation and back propagation (BP) neural network prediction models are established based on the slab method and the rolled measured data, respectively. Furthermore, to solve the model's black‐box problem while also improving thickness prediction accuracy, three combined prediction models are built using the principles of compensating multiplicative error, additive error, and average error based on the model's advantages and features for thickness prediction. During the modeling phase, 1925 groups of manufacturing data for titanium/steel‐clad plate rolling are selected and normalized to form the data set. The trial‐and‐error method is used to determine the optimal network structure for the BP neural network. The model's performance is assessed by calculating the mean square error, mean absolute error, and coefficient of determination (R2) and comparing the relative errors in prediction outcomes across different models. In the results, it is demonstrated that the average error combined model (Combined Model III) performs better and predicts more accurately.