In this paper， a landslide displacement prediction model integrating extreme gradient boosting and optimized support vector regression is proposed by using extreme gradient boosting and support vector regression， and combining the advantages of hunter-prey optimization algorithm. Firstly， extreme gradient boosting （XGBoost） is used for the preliminary prediction of landslide displacement， and then hunter-prey optimizer （HPO） is used to optimize support vector regression （SVR）. A combined prediction model （HPO-SVR） is constructed by optimizing the hyperparameters of SVR using HPO to correct the prediction results of XGBoost. The validation of two sets of landslide displacement measured data shows that the HPO algorithm obtains a more reasonable hyperparameter of SVR through the dynamic optimization strategy of constantly updating the positions of the hunter and the prey. Relative to the combined prediction models of XGBoost， SVR， and its combination with particle swarm optimization algorithm， genetic algorithm， and HPO， the combined XGBoost-HPO-SVR model achieves good results in predicting the displacements of Yangwashan landslide and Tuojiashan landslide， with mean square errors of 3.505 and 0.550， and mean absolute errors of 1.357 and 0.538， respectively.