This paper develops an artificial neural network (ANN) model for predicting the speed limit of cars moving on corroded steel girder bridges. A total of 311 datasets, which are created from the proposed analytical model, are used to construct the ANN model. The input parameters of the proposed ANN model include the car’s weight, diameter of tires, and the dimensions of girder bridges, which are the top flange width, top flange thickness, bottom flange width, bottom flange thickness, girder height, web thickness, and the span of girder. Meanwhile, the speed limit of cars is the output variable of the ANN model. The results show that the speed limitation of cars on the corrosive steel girder bridge is reduced pronounced after 100 years. Sensitivity analyses reveal that the influential parameters with respect to the maximum speed are the girder height and tire diameter, whereas the girder weigh and girder span have negative effects on the speed limit of cars. Moreover, a mathematical formula and a graphical user interface program are developed to calculate the speed limits of cars on the corrosive steel girder bridge. These practical tools are very helpful for practitioners in determining the speed limit of cars moving on steel girder bridges subjected to corrosion.