  The application of machine learning (ML) in high energy physics (HEP),
specifically in heavy-flavor jet tagging at Large Hadron Collider (LHC)
experiments, has experienced remarkable growth and innovation in the past
decade. This review provides a detailed examination of current and past ML
techniques in this domain. It starts by exploring various data representation
methods and ML architectures, encompassing traditional ML algorithms and
advanced deep learning techniques. Subsequent sections discuss specific
instances of successful ML applications in jet flavor tagging in the ATLAS and
CMS experiments at the LHC, ranging from basic fully-connected layers to graph
neural networks employing attention mechanisms. To systematically categorize
the advancements over the LHC's three runs, the paper classifies jet tagging
algorithms into three generations, each characterized by specific data
representation techniques and ML architectures. This classification aims to
provide an overview of the chronological evolution in this field. Finally, a
brief discussion about anticipated future developments and potential research
directions in the field is presented.
