In the present work, we developed an ML model, namely RedPred, that predicts the redox reaction energy which is a highly correlated descriptor with the experimental redox potential of AORFB electrolyte materials . To train our models, we used RedDB , a recently published materials dataset that contains candidate AORFB reactant-product pairs calculated by accurate quantum chemical methods. We evaluated the performance of the proposed model on two reserved test sets where the first set covers the same chemical space of the training set and the second set falls outside the chemical space to assess its extrapolation ability. We compared the performances of the models trained on different ML algorithms and molecular encoders. Based on the new learnings, we developed RedPred which comprises an ensemble of Artificial Neural Networks (ANN), Random Forests (RF), and Graph Convolutional Networks (GCNs). RedPred showed promising results not only within the training chemical space but also outside of it. Importantly, we provided insights about the difficulties of extrapolating outside of the training domains. We released RedPred as an online web tool, along with its source code, for public use with the aim of expediting the search of prospective AORFB electrolyte materials as a complementary tool.