  Extracting bounds on BSM operators at hadron colliders can be a highly
non-trivial task. It can be useful or, depending on the complexity of the event
structure, even essential to employ modern analysis techniques in order to
measure New-Physics effects. A particular class of such modern methods are
Machine-Learning algorithms, which are becoming more and more popular in
particle physics. We attempt to gauge their potential in the study of
$Vh(\rightarrow b\bar b)$ production processes, focusing on the leptonic decay
channels of the vector bosons. Specifically, we employ boosted decision trees
using the kinematical information of a given event to discriminate between
signal and background. Based on this analysis strategy, we derive bounds on
four dimension-6 SMEFT operators and subsequently compare them with the ones
obtained from a conventional cut-and-count analysis. We find a mild improvement
of $\mathcal{O}(\mathrm{few}\, \%)$ across the different operators.
