Accurately predicting the behavior of a nuclear reactor requires multiphysics simulation of coupled neutronics, thermal-hydraulics and fuel thermo-mechanics. The fuel thermo-mechanical response provides essential information for operational limits and safety analysis. Traditionally, fuel performance analysis is performed standalone, with spatial-temporal power distribution and thermal boundary conditions calculated from the coupled neutronics-thermal-hydraulics simulation used as input. Such limited one-way coupling is result of cost induced by the full-core fuel performance analysis, which provides more realistic and accurate prediction of the core-wide response than the “peak rod” analysis. The computational burden for full-core fuel performance simulation falls within 12 hours in serial for NRC-licensed code FRAPCON, while becomes unbearable for high fidelity codes like BISON. Therefore, it is desirable to improve the computational efficiency of full-core fuel performance modeling by constructing fast-running surrogate. As such we can utilize fuel performance modeling in the core reload design optimization and improve neutron economy. This work thereby presents methodologies for full-core surrogate construction based on several realistic equilibrium PWR core designs. As a fast and conventional approach, look-up tables (LUTs) are only effective for certain fuel performance quantities of interest (QoIs). Several representative machine-learning (ML) algorithms are therefore introduced to capture the complicated physics for other fuel performance QoIs. Rule-based model is useful as a feature extraction technique to account for the spatial-temporal complexity of the operating conditions. Constructed surrogates achieve at least ten thousand time acceleration compared to FRAPCON with satisfying prediction accuracy. Current work lays foundation for tighter coupling of the fuel performance analysis into the core design optimization framework. It also sets stage for full-core fuel performance analysis with higher fidelity tools like BISON where the computational cost becomes more burdensome.