In view of the complexity and diversity of the disaster causing factors that cause debris flow disasters, resulting in the excessive dimension of the model input data and the problem that extreme gradient boosting is easy to fall into local optimization, resulting in the low accuracy of the prediction model. A debris flow disaster prediction method based on GWO-XGBoost model is proposed. First, the original data collected by the sensor is preprocessed to obtain the standard data, and then the dimension of the data is reduced by linear discriminant analysis, and the disaster causing factors with low coupling and high contribution rate are obtained as the model input to predict whether the debris flow disaster occurs; Secondly, grey wolf optimizer is used to optimize the super parameters of the model; Finally, Mozigou monitoring data are used for simulation verification. The results show that the normalized data after preprocessing and dimensionality reduction by linear discriminant analysis algorithm solves the problem of dimensionality disaster of model input. The prediction accuracy of GWO-XGBoost debris flow disaster prediction model is 96.64%, which is 6.69%, 5.13% and 3.86% higher than that of random forest model, support vector machine model and xgboost model respectively, It enriches the prediction methods of debris flow disasters and provides new ideas for relevant decisionmaking departments.