The automated QM-based workflow computes C-H hydricities with an MAE of 4.43 kcal/mol and RMSE of 5.45 kcal/mol when correlating with experimental hydricities. We use the linear relationship to generate training data for a regression model based on CM5 atomic charges that produce ML predicted hydricities. When correlating with QM computed hydricities, we obtain an MAE and RMSE of 2.30 kcal/mol and 3.74 kcal/mol, respectively. The QM-based workflow and regressor are freely available under the MIT license. We can predict the reaction site for 250 reactions with equivalent performance for the QM-based workflow (MCC: 0.52), the regressor (MCC: 0.50), and ALFABET (MCC: 0.53). By splitting the reaction dataset into categories, it is evident that hydride transfers control some reactions, and others are controlled by radical transfers. For oxidative degradation and H abstraction C-X, ALFABET outperforms our methods, but for H abstraction C-N and oxidation, the QM workflow is the best-performing method. This aligns with the literature where a radical intermediate controls the former, and the latter is controlled by a hydride intermediate. Surprisingly, ALFABET performs poorly for H abstraction C-C, which is controlled by radical intermediates. On the other hand, ALFABET performs similarly to the hydride transfer reactions in miscellaneous, which may indicate that some of the reactions undergo radical intermediates or that the carbocation/radical stability follows the same rules. All methods have low performance for the carbene insertion reactions, which indicates that steric effects play a significant role. Neither of our workflows provides as high precision as our previous study on C-H pK a values , which may be due to the QM workflow being based on fewer experimental measurements and the higher complexity of the reactions we compare with (most mechanisms are not known, bulky catalysts, etc.). However, we believe both the QM workflow and ML model can be used to estimate the reaction site for reactions that undergo a carbocation-like intermediate. We also encourage investigating BDEs as a slight alteration of the reaction conditions may change the mechanism. Future work will focus on enhancing the predictability of reaction sites by expanding the training data and adding steric effects to the descriptor.