  We develop an efficient machine learning protocol to predict the
noise-induced coherence from the nonequilibrium fluctuations of photon exchange
statistics in a quantum heat engine. The engine is a four-level quantum system
coupled to a unimodal quantum cavity. The nonequilibrium fluctuations
correspond to the work done during the photon exchange process between the
four-level system and the cavity mode. We specifically evaluate the mean,
variance, skewness, and kurtosis for a range of engine parameters using a full
counting statistical approach combined with a quantum master equation
technique. We use these numerically evaluated cumulants as input data to
successfully predict the hot bath induced coherence. A supervised machine
learning technique based on K-Nearest Neighbor(KNN) is found to work better
than a variety of learning models that we tested.
