  Several discrepancies in the decay of B-meson decay have drawn a lot of
interest in the leptoquarks (LQ), making them an exciting discovery. The
current research aims to discover the pair-production of leptoquarks that links
strongly to the third generation of quarks and leptons at the center of mass
energy $\sqrt{s}$=14 TeV, via proton-proton collisions at the Large Hadron
Collider (LHC). Based on the lepton-quark coupling parameters and branching
fractions, we separated our search into various benchmark points. The leading
order (LO) signals and background processes are generated, while parton
showering and hadronization is also performed to simulate the detector effects.
The Boosted Decision Trees (BDTs), Multilayer Perceptron (MLP), and Likelihood
(LH) methods are effective in improving signal-background discrimination
compared to traditional cut-based analysis. The results indicate that these
machine learning methods can significantly enhance the sensitivity in probing
for new physics signals, such as LQs, at two different integrated luminosities.
Specifically, the use of BDTs, MLP, and LH has led to higher signal
significances and improved signal efficiency in both hadronic and semi-leptonic
decay modes. The results suggest that the LQ masses of 500 GeV and 2.0 TeV in
fully hadronic decay modes can be accurately probed with signal significance
176.70 (17.6) and 184.27 (0.01) for MVA (cut-based) at 1000 $fb^{-1}$,
respectively. Similarly, in semi-leptonic decay mode the signal significance
values are 168.56 and 181.89 at lowest and highest selected LQ masses
respectively for MVA method only. The enhanced numbers by a factor of 2 are
also reported at 3000 $fb^{-1}$.
