Stress corrosion cracking (SCC) as a potential failure mechanism endangers structural integrity of the nickel-base 690 alloy components and welds that are widely used in the high temperature and high pressure water environment in pressurized water reactors (PWRs). Due to the complexity of the interweaving influences, the existing parameterized prediction models developed for SCC are limited for engineering assessment by rather lower accuracy. In this study, a Knowledge-Based Random Forest (KBRF) model was developed for predicting the SCC growth rate of the nicked-base 690 alloy through combining random forest machine learning algorithm (RF) with domain knowledge-based MRP-386 parameterized model. It was found that the robustness and accuracy of the KBRF model were significantly improved, in comparison with the MRP-386 parameterized model and the RF machine learning model by introducing domain knowledge into the machine learning modeling. The results demonstrate potential engineering application of the presented model on SCC growth rate prediction of nicked-base 690 alloy components and welds in PWRs.