  A probing scheme is considered with an accessible and controllable qubit,
used to probe an out-of equilibrium system consisting of a second qubit
interacting with an environment. Quantum spontaneous synchronization between
the probe and the system emerges in this model and, by tuning the probe
frequency, can occur both in-phase and in anti-phase. We analyze the capability
of machine learning in this probing scheme based on quantum synchronization. An
artificial neural network is used to infer, from a probe observable, main
dissipation features, such as the environment Ohmicity index. The efficiency of
the algorithm in the presence of some noise in the dataset is also considered.
We show that the performance in either classification and regression is
significantly improved due to the in/anti-phase synchronization transition.
This opens the way to the characterization of environments with arbitrary
spectral densities.
