  Scenarios seeking to address the issue of electroweak symmetry breaking often
have heavy colored gauge bosons coupling preferentially to the top quark.
Considering the bulk Randall-Sundrum as a typical example, we consider the
prospects of the first Kaluza-Klein mode ($G^{(1)}$) of the gluon being
produced at the LHC in association with a $t \bar{t}$ pair. The enhanced
coupling not only dictates that the dominant decay mode would be to a $t
\bar{t}$ pair, but also to a very large $G^{(1)}$ width, necessitating the use
of a renormalised $G^{(1)}$ propagator. This, alongwith the presence of large
backgrounds (specially $t \bar{t} j j$), renders a conventional cut-based
analysis ineffective, yielding only marginal significances of only around
2$\sigma$. The use of Machine Learning (ML) techniques alleviates this problem
to a great extent. In particular, the use of Artificial Neural Networks helps
us identify the most discriminating observables, thereby allowing a
significance in excess of 4$\sigma$ for $G^{(1)}$ masses of $\sim$ 4 TeV.
