Water level prediction is an auxiliary decision support for flood warning work. For accurate water level prediction and providing scientific basis for natural disaster prevention， a prediction model combining Modified Gray Wolf Optimization （MGWO） algorithm and Temporal Convolutional Network （TCN） was proposed， namely MGWO-TCN. In view of the shortage of premature and stagnation in the original Gray Wolf Optimization （MGWO） algorithm， the idea of Differential Evolution （DE） algorithm was introduced to extend the diversity of the grey wolf population. The convergence factor during update and the mutation operator during mutation of the grey wolf population were improved to adjust the parameters in the adaptive manner， thereby improving the convergence speed and balancing the global and local search capabilities of the algorithm. The proposed MGWO algorithm was used to optimize the important parameters of TCN to improve the prediction performance of TCN. The proposed prediction model MGWO-TCN was used for river water level prediction， and the Root Mean Square Error （RMSE） of the model’s prediction results was 0.039. Experimental results show that compared with the comparison model， the proposed MGWO-TCN has better optimization ability and higher prediction accuracy.