  We propose a machine learning approach based on artificial neural network to
gain faster insights on the role of geometric contributions to the
nonequilibrium fluctuations of an adiabatically temperature-driven quantum heat
engine coupled to a cavity. Using the artificial neural network we have
explored the interplay between bunched and antibunched photon exchange
statistics for different engine parameters. We report that beyond a pivotal
cavity temperature, the Fano factor oscillates between giant and low values as
a function of phase difference between the driving protocols. We further
observe that the standard thermodynamic uncertainty relation is not valid when
there are finite geometric contributions to the fluctuations, but holds true
for zero phase difference even in presence of coherences.
