In summary, we have developed a fast MCR of acyclic or cyclic N-sulfonylimines that was used as a representative reaction type to develop ML models for predicting reaction outcomes in a blind prospective manner. The fast and peculiar reactivity mechanism of N-sulfonylimines was explained using DFT calculation to understand the critical role of transition states and intermediates. Bootstrapped decision tree-based ML models resulted in a chemical reactivity flowchart that explained the choices made by the model to predict reaction outcomes. The human interpretable ML approach can be extended to explore any MCR or any chemical reaction used to synthesize a library of compounds in a quick and efficient manner. This work provides a framework for developing fast MCRs, understanding the underlying reaction mechanism and identifying chemical features for predicting the reactivity of components that results in successful reactions to save valuable time for chemists to not chase deadend leads.