  Optimizing the probability of quantum tunneling between two states, while
keeping the resources of the underlying physical system constant, is a task of
key importance due to its critical role in various applications. We show that,
by applying Machine Learning techniques when the system is coupled to an
ancilla, one optimizes the parameters of both the ancillary component and the
coupling, ultimately resulting in the maximization of the tunneling
probability. We provide illustrative examples for the paradigmatic scenario
involving a two-mode system and a two-mode ancilla in the presence of several
interacting particles. Physically, the increase of the tunneling probability is
rooted in the decrease of the two-well asymmetry due to the coherent
oscillations induced by the coupling to the ancilla. We also argue that the
enhancement of the tunneling probability is not hampered by weak coupling to
noisy environments.
