Various classification of aluminum alloys is one of the most common applied materials in transportation industries due to the specific mechanical and material properties. Body of vehicles, including cars, ships, and airplanes, are constantly exposed to different cyclic loads that lead to surface damage and finally, fatigue failure occurs by crack growth. Therefore, the quality of the machined surface has a direct effect on the fatigue life of the products. In this regard, the most important source of surface roughness is the choice of machining parameters. To understand it, turning operations were performed on 2xxx and 7xxx series aluminum alloys considering different values of process parameters. For any series of aluminums, 189 high-cycle fatigue testing specimens were prepared, and experiments were performed by axial tension–compression fatigue test machine in 7 levels of stress (all tests were repeated three times and the mean failure cycles were reported as the fatigue life). Next, an Artificial Neural Network (ANN) based on the Back Propagation (BP) error algorithm was developed to predict fatigue life of Al alloys which are machined with different conditions. To this end, the parameters considered as input variables to the neural network structure include the Yield Strength (YS) and Ultimate Tensile Strength (UTS) to identify the aluminum series, as well as the process parameters such as cutting depth (d), rotational speed (R), and feed speed (V). In addition, the applied cyclic stress (S) to specimen was considered as input. Furthermore, surface roughness (Z) and number of cycles to failure (N) were considered as ANN output. The comparison of the obtained results from ANN predicting with the experimental values indicates that this approach was tuned finely. Eventually, the presented model can be a suitable alternative to perform fatigue tests with high costs and time-consuming. Also, the most effective machining parameter on the surface roughness and fatigue limit was reported.