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In order to solve the complex adjustment of XGBoost model parameters and further improve the accuracy of water quality data prediction，a predictin algorithm based on Gray Wolf Optimization（GWO） to optimize XGBoost. First，taking advantage of the strong convergence ability of the grey wolf optimization algorithm for global parameter search，we use GWO to optimize the hyperparameters of XGBoost（the maximum number of spanning trees，the learning rate，and the maximum depth of the tree） to obtain the best prediction performance of XGBoost. Secondly，the data reliability was improved by pre-processing the critical water quality data，and various algorithms are used for comparative analysis experiments. The results showed that compared with LSTM and XGBoost without GWO optimization parameters，the optimized XGBoost model had better nonlinear prediction ability，and the determination factor R2 of the model reached above 0.85.