  We discuss a novel approach based on machine learning (ML) regression models
to reduce the computational cost of disconnected diagrams in lattice QCD
calculations. This method creates a mapping between the results of fermionic
loops computed at different quark masses and flow times. The ML model, trained
with just a small fraction of the complete data set, provides similar
predictions and uncertainties over the calculation done over the whole
ensemble, resulting in a significant computational gain.
