The self-propelled rotary tool turning (SPRT) process is an effective solution for machining hardened steels. In this investigation, the specific cutting energy (SCE) model was developed in terms of the inclination angle (I), depth of cut (D), feed rate (f), and spindle speed (S). A set of experiments was performed for the SKD 61 material to obtain experimental data. The Bayesian regularized feed-forward neural network was applied to develop the SCE model. The results indicated that the model’s precision was acceptable due to the small deviations between the predictive and actual data. Moreover, the proposed correlation was primarily affected by the depth of cut, feed rate, spindle speed, and inclination angle, respectively. Finally, the developed SPRT operation could be utilized for machining difficult-to-cut materials.