Automating the discovery of chemical reaction mechanisms can increase the efficiency of the use of experimental data to obtain chemical knowledge. In this study, a sparse identification approach was employed to determine reaction mechanisms, providing accurate and interpretable kinetic models while preventing overfitting. The main advantage of the proposed approach over conventional sparse identification algorithms is that it can be applied to cases with limited concentration profiles, which often occur for chemical reactions involving untraceable intermediates. To demonstrate its applicability to complex reaction mechanisms beyond the reach of classical kinetic analysis, the autocatalytic reduction of manganese oxide ions was selected as the target reaction. Although the concentrations of only two manganese species could be monitored via UV‒vis absorption spectroscopy, the experimental data were sufficiently represented by 11 elementary steps involving 8 chemical species. This strategy enables the automated discovery of reaction mechanisms without relying on heuristic kinetic models, as the only assumption required is the composition of the intermediates.