This work presents SurQCT, a novel and general surrogate approach to constructing state-to-state (StS) kinetics databases using machine learning. The surrogate comprises three components: i) a feed-forward neural network (FNN) that relies on physical insights related to the centrifugal barrier for predicting dissociation rate coefficients, and ii) two deep operator networks (DeepONets) for the inelastic and exchange reaction rate coefficients.