Mach number is a key metric in the evaluation of wind tunnel flow field performance. This complex process of wind tunnel test mainly has the problems of nonlinearity and time lag. In order to overcome the problems and control the Mach number stability, this paper proposes a new method of Mach number prediction based on a nonlinear autoregressive exogenous-genetic algorithm-Elman (NARX-GA-Elman) model, which adopts NARX as the basic framework, determines the order of the input variables by using the false nearest neighbor (FNN), and uses the dynamic nonlinear network Elman to fit the model, and finally uses the global optimization algo-rithm GA to optimize the weight thresholds in the model to establish the Mach number prediction model with optimal performance under single working condition. By comparing with the traditional algorithm, the predic-tion accuracy of the model is improved by 61.5%, and the control accuracy is improved by 55.7%, which demonstrates that the model has very high prediction accuracy and good stability performance.