  Recently, machine learning (ML) techniques have led to a range of numerous
developments in the field of nuclear and high-energy physics. In heavy-ion
collisions, the impact parameter of a collision is one of the crucial
observables which has a significant impact on the final state particle
production. However, calculation of such a quantity is nearly impossible in
experiments as the length scale ranges in the level of a few fermi. In this
work, we implement the ML-based regression technique via Boosted Decision Tree
(BDT) to obtain a prediction of impact parameter in Pb-Pb collisions at
$\sqrt{s_{\rm NN}}$ = 5.02 TeV using A Multi-Phase Transport (AMPT) model. In
addition, we predict an event shape observable, transverse spherocity in Pb-Pb
collisions at $\sqrt{s_{\rm NN}}$ = 2.76 and 5.02 TeV using AMPT and PYTHIA8
based on Angantyr model. After a successful implementation in small collision
systems, the use of transverse spherocity in heavy-ion collisions has potential
to reveal new results from heavy-ion collisions where the production of a QGP
medium is already established. We predict the centrality dependent spherocity
distributions from the training of minimum bias simulated data and it was found
that the predictions from BDT based ML technique match with true simulated
data. In the absence of experimental measurements, we propose to implement
Machine learning based regression technique to obtain transverse spherocity
from the known final state observables in heavy-ion collisions.
