  Nowadays, there has been a growing trend in the fields of high-energy physics
(HEP) in its both parts experimental and phenomenological studies, to
incorporate machine learning (ML) and its specialized branch, deep learning
(DL). This review paper provides a thorough illustration of these applications
using different DL approaches. The first part of the paper examines the basics
of various particle physics types and sets up guidelines for assessing particle
physics alongside the available learning models. Next, a detailed
classification is provided for representing the jet images that are
reconstructed in high energy collisions mainly with proton-proton collisions at
well defined beam energies, covering various datasets, preprocessing
techniques, and feature extraction and selection methods. The presented
techniques can be applied to future hadron-hadron colliders (HLC) such as high
luminosity LHC (HL-HLC) and future circular collider-hadron-hadron (FCC-hh).
Next, the authors explore a number of AI models analysis designed specifically
for images in HEP. We additionally undertake a closer look at the
classification associated with images in hadron collisions, with an emphasis on
Jets. In this review, we look into various state-of-the-art (SOTA) techniques
in ML and DL, examining their implications for HEP demands. More precisely,
this discussion tackles various applications in extensive detail, such as Jet
tagging, Jet tracking, particle classification, and more. The review concludes
with an analysis of the current state of HEP, using DL methodologies. It covers
the challenges and potential areas for future research that will be illustrated
for each application.
