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Data-driven approach to elucidate the correlation between photocatalytic activity and rate constants from excited states This work presents a data-driven framework that uncovers how excited-state kinetics govern the catalytic activity of organic photosensitizers by integrating machine learning with quantum chemical calculations.Theoretically simulated rate constants from excited states were shown to outperform experimentally measured excited-state lifetimes as descriptors.SHAP-based visual analyses revealed that transitions from the T1 state, together with the non-radiative decay process between the S1 and S0 states, play key roles in determining photocatalytic activity.This computational approach provides insights that are not readily accessible through conventional experimental methods.