  Machine Learning (ML) techniques have been employed for the high energy
physics (HEP) community since the early 80s to deal with a broad spectrum of
problems. This work explores the prospects of using Deep Learning techniques to
estimate elliptic flow ($v_2$) in heavy-ion collisions at the RHIC and LHC
energies. A novel method is developed to process the input observables from
particle kinematic information. The proposed DNN model is trained with Pb-Pb
collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV minimum bias events simulated with
AMPT model. The predictions from the ML technique are compared to both
simulation and experiment. The Deep Learning model seems to preserve the
centrality and energy dependence of $v_2$ for the LHC and RHIC energies. The
DNN model is also quite successful in predicting the $p_{\rm T}$ dependence of
$v_2$. When subjected to event simulation with additional noise, the proposed
DNN model still keeps the robustness and prediction accuracy intact up to a
reasonable extent.
