High Temperature Superconducting (HTS) cables are promising devices for electrical power transmission of renewable energy resources, where their fault performance study is vital to avoid interruptions in electric system. In this study, fast intelligent surrogate models were presented to estimate the fault performance of a 22.9 kV/50 MW HTS cable to make the fast fault performance analysis of the HTS cables possible, during the design stages. Different fault scenarios were considered under different fault durations, fault resistances, and types of faults. Then, fault energy, fault current, fault type, fault duration, and fault resistances were fed into surrogate model, as inputs. On the other hand, the outputs were temperature of ReBCO tapes, former temperature, ReBCO layer current, and total resistance of each phase. For surrogate modelling, Cascade Forward Neural Networks (CFNN) was used. Results show that the accuracy of CFNN-based model to estimate fault performance of the cable with an average accuracy of 99.1%. Finally, the impact of considering fault energy, fault current, and both, as the inputs of the models, on the final accuracy was explored. Results showed that fault energy consideration could increase the accuracy of surrogate model.