Severe shot peening (SSP) process is widely used for surface nanocrysallization of a bulk material that demonstrates excellent mechanical properties compared with its coarse-grained equivalents. In this study, a plastically deformed surface was produced with nanostructured grains on different materials of AISI 1045, 1050, and 1060 carbon steels by means of SSP. Shot peening was applied with a wide range of Almen intensities and coverages. Optical microscopy, scanning electron microscopy, field emission scanning electron microscopy, high resolution transmission electron microscope observations, and X-ray diffraction analysis were employed to analyze the mechanism of grain refinement experimentally as well as the surface roughness and residual stress measurements. Afterwards, different shot peening treatments were used to develop a novel alternative approach based on artificial neural network (ANN) for modelling as well as parametric and sensitivity analysis of grain refinement and surface roughness. The experimental results were utilized to implement the ANN. The modelling results indicated that the neural network-based approach can be used to effectively analyze nanocrystallization and roughness variations of the shot peened carbon steels.