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ABSTRACT: Jet tagging is an important task in high-energy physics, where the goal is to identify jets of particles produced in particle collisions. Machine learning has proven to be a powerful tool in this field, allowing for improved accuracy and efficiency in jet tagging. In this talk, we provide a comprehensive review of the state-of-the-art machine learning techniques used in jet tagging. We categorize these techniques into three main representation types: image-based, particle-based, and point cloud-based. For each representation, we discuss the corresponding neural network architectures, including CNNs, 1D-CNNs, RNNs, Deep set frameworks, ParticleNets, ABCNets, LorentzNets, and Transformers. We also introduce a new architecture which incorporates the pairwise particle interaction and the pairwise jet feature interaction to the Point Cloud Transformer , called P-DaViT. This talk aims to provide an overview of the current landscape of machine learning applications in jet tagging. https://indico.kias.re.kr/event/198/