The surrogate model is an effective way to connect the simulation and optimization models in groundwater flow numerical modeling; it could overcome the limitations of embedding and calling simulation models in the optimization model by conventional methods, which greatly reduces the computational load caused by directly calling the simulation model in the solving process of the optimization model. In this paper, the dual-response surface method and radial basis function artificial neural network method were applied to establish the surrogate model of groundwater flow numerical simulation in Jinquan Industrial Park, Inner Mongolia, China. The Latin hypercube sampling method was used to determine random pumping load of the five pumping wells, which were taken as the input data groundwater flow numerical simulation model for calculating 10 observation wells drawdown data sets (output data sets). Based on the input and output data sets, the dual-response surface method and radial basis function artificial neural network method were used to establish the surrogate model of groundwater simulation model, and the validity of surrogate models were comparatively tested. The results showed that both the results of two surrogate models fit well with the results of the simulation model, which indicates that two surrogate models were capable of approaching the groundwater flow numerical simulation model; compared with the dual response surface model, the RBF neural network model had more advantages in terms of sample size requirements, fitting the accuracy of simulation results.