  We study the impact of machine-learning algorithms on LHC searches for
leptoquarks in final states with hadronically decaying tau leptons, multiple
$b$-jets, and large missing transverse momentum. Pair production of scalar
leptoquarks with decays only into third-generation leptons and quarks is
assumed. Thanks to the use of supervised learning tools with unbinned methods
to handle the high-dimensional final states, we consider simple selection cuts
which would possibly translate into an improvement in the exclusion limits at
the 95$\%$ confidence level for leptoquark masses with different values of
their branching fraction into charged leptons. In particular, for intermediate
branching fractions, we expect that the exclusion limits for leptoquark masses
extend to $\sim$1.3 TeV. As a novelty in the implemented unbinned analysis, we
include a simplified estimation of some systematic uncertainties with the aim
of studying their possible impact on the stability of the results. Finally, we
also present the projected sensitivity within this framework at 14 TeV for 300
and 3000 fb$^{-1}$ that extends the upper limits to $\sim$1.6 and $\sim$1.8
TeV, respectively.
