To meet the increasing accuracy and speed requirements for strip crown prediction, a strip crown prediction model based on principal component analysis (PCA) and the random forest (RF) algorithm was established in this paper. Firstly, the pauta criterion was used to remove outliers, and the five-point cubic smoothing formula was employed for noise reduction. Secondly, the dimension of the data was reduced using principal component analysis, and the load matrix was utilized to select the key control variables. Finally, a strip crown prediction model based on random forest was established by utilizing the key control variables. The model was compared with the support vector regression (SVR), K-Nearest Neighbor (KNN), Gradient Boosting Decision Tree (GBDT), extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM) models. The results showed that the PCA-RF model reduced the parameters from LightGBM dimensions to 15 dimensions, significantly reducing the modeling time. The coefficient of determination (R2), mean absolute error (MAE), and root mean squared error (RMSE) of PCA-RF based on the test set were 0.982 0, 1.485 2 μm and 2.260 3 μm, respectively. The prediction error of over 98% of sample points was within the range of -3~3 μm, which meets the accuracy requirements for strip crown prediction. Additionally, to further verify the prediction stability of the model, the distribution of R2, MAE, and RMSE remained in the optimal position after 100 model operations. It is concluded that the PCA-RF model can reduce modeling time through dimensionality reduction while achieving high-precision strip crown prediction, providing a reference for further studies on strip crown.