With the growing global focus on environmental protection and carbon emissions, electric vehicles (EVs) are becoming increasingly popular. Permanent magnet synchronous motors (PMSMs) have emerged as a core component of the drive system due to their high-power density and compact design. The rotor temperature of PMSMs significantly affects their operating efficiency, management strategies, and lifespan. However, real-time monitoring and acquisition of rotor temperature are challenging due to cost and space limitations. Therefore, this study proposes a hybrid model named RIME-XGBoost, which integrates the RIME optimization algorithm with XGBoost, for the precise modeling and prediction of PMSM rotor temperature. RIME-XGBoost utilizes easily monitored dynamic parameters such as motor speed, torque, and currents and voltages in the d-q coordinate system as input features. It simultaneously optimizes three hyperparameters (number of trees, tree depth, and learning rate) to achieve high learning efficiency and good generalization performance. The experimental results show that, on both medium-scale datasets and small-sample datasets in high-temperature ranges, RIME-XGBoost outperforms existing methods such as SMA-RF, SO-BiGRU, and EO-SVR in terms of RMSE, MBE, R-squared, and Runtime. RIME-XGBoost effectively enhances the prediction accuracy and computational efficiency of rotor temperature. This study provides a new technical solution for temperature management in EVs and offers valuable insights for research in related fields.