Stress corrosion cracking (SCC) endangers structural integrity of the nickel-base alloy 600 components widely used in the water environment of high temperature and high pressure in pressurized water reactors (PWRs). Due to the complexity of the interweaving influences, the existing prediction models developed for SCC are limited for engineering assessment by accuracy. In this study, a non-algebraic model with multi-dimensional data associations was developed for predicting the SCC growth rate of the Ni-base alloy 600, which utilized the TPE-XGBoost machine learning algorithm to describe the correlation between the multiple characteristic parameters including stress intensity factor, temperature, yield strength, dissolved hydrogen content, crack propagation direction, load type, heat treatment process and SCC growth rate. It was found that the TPE-XGBoost algorithm could achieve rapid global optimization of multi-dimensional data sets rather than the local optimal values. The obtained SCC model with sound generalization ability demonstrates potential engineering application on SCC growth rate prediction of Ni-base alloy 600 components in PWRs.