Most commercial power reactors in the world, so called second generation of nuclear power plants (NPP), were designed in 1960s and 1970s. Due to technology constrains, these NPP's nuclear fuel burnup data are calculated as a whole of a fuel assembly (FA) based on the total core power output during certain period of time and the theoretical physics calculation of the thermal neutron flux (TNF) distribution in the reactor core. This traditional burnup calculation based on theoretical TNF 3-D distribution for each FA in the core is far less accurate in term of pin-point burnup data along the entire length of a FA. Therefore, the most contribution factor to fuel failure event, e.g. the accurate burnup data at a fine grained location along a FA, could not be obtained by these existing methods and practice in these NPPs.This invention applies the modern machine learning and artificial intelligent methods to provide a much finer-grained TNF 3D distribution prediction for these second generation NPPs. With this pin-point TNF data along each FA's length, the maximum burnup locations in the entire core can be determined. This will result a more accurate method for determine the fuel failure locations after fuel failure events.