The present study proposes the use of Machine Learning to model turbulent heat transfer in liquid metal cooled nuclear reactors. Traditional modelling methods are found to have limitations, and the availability of high-fidelity data for low Prandtl number fluids motivates the use of advanced regression techniques. The work is based on the analysis and the extension of the data-driven model developed by M. Fiore to simulate liquid metal heat transfer. The original Neural Network model was trained with high-fidelity data only. This approach was found to be limiting when the model is coupled with common momentum Reynolds Averaged Navier Stokes (RANS) closures due to the inconsistency between Direct Numerical Simulation (DNS) and RANS turbulence input data.