Abstract: The cumulative damage in the composite structures during the fatigue process will lead to decrease in the stiffness of structures. This will further cause the changes in the dynamic parameters of the structure, such as the frequencies. Therefore, the fatigue status can be related to the frequencies of structures, and inversely, the frequencies can be used to predict the fatigue life of the structures. In this paper, by incorporating Umat subroutines in ABAQUS, a three-dimensional finite element model was developed to simulate the evolution of fatigue damage in glass fiber reinforced polymer (GFRP) laminates. The fatigue characteristics of composite materials in the longitudinal, transverse and in-plane shear directions have been considered in the model. The fatigue state of the glass fiber reinforced polymer laminates was exported from the fatigue model and then was imported into a second model to apply modal analysis for obtaining the frequencies which are corresponding to the fatigue status. An artificial neural network was trained based on the frequency data during the fatigue process, with the frequency shifts as the input and the remaining fatigue life as the output. The training data for ANN was generated from the finite element (FE) models, based on which the remaining fatigue life of the GFRP laminates was predicted. In particular, to consider the discreteness of material properties of fiber reinforced polymer (FRP) composite materials, each element in the finite element model (FEM) was individually assigned the material properties randomly distributed in the Gaussian normal distribution. The results show that the numerical simulation results of the present fatigue model are consistent with the fatigue test data in the existing literature. The trained artificial neural network can successfully predict the remaining fatigue life of the GFRP laminates.