High precision network traffic prediction is the basis of modern network intelligent management. Targeting at the problem of parameter optimization of SVM in the process of network traffic prediction modeling to improve the network traffic prediction results, this paper proposes the network traffic prediction model of SVM optimized by Improved Gray Wolf algorithm. Firstly, collect the historical data of network traffic, and preprocess the data with phase space reconstruction and normalization, then introduce the improved gray wolf algorithm to quickly search the relevant parameters of the global optimal support vector machine, and learn the historical data of network traffic after preprocessing according to the optimal parameters, and establish a prediction model that can mine the history data of network traffic including the law of change after that, the network traffic prediction model of SVM optimized by other algorithms is compared and analyzed. The results show that the prediction accuracy of the improved gray wolf algorithm optimized support vector machine is more than 90%, much higher than the compared model, and the training time of the prediction modeling process is less than the compared model, which can meet the requirements of high accuracy and realtime network traffic management.