This presentation explores the application of classical and quantum machine learning techniques in particle physics, highlighting their role in tasks such as jet classification, Higgs boson and SUSY identification, and fully hadronic event generation. It begins with an overview of the historical development of artificial intelligence and foundational concepts in deep learning, followed by practical implementations of deep neural networks (DNNs) and convolutional neural networks (CNNs) in high-energy physics analyses, including supersymmetry searches and jet substructure studies. The presentation then introduces quantum machine learning (QML), covering models such as quantum support vector machines (QSVMs) and variational quantum classifiers (VQCs), along with their advantages and challenges in Higgs boson identification. Finally, the use of generative AI is presented as a solution for data generation in both Standard Model and beyond Standard Model scenarios, enabling the production of synthetic datasets significantly faster than traditional Monte Carlo simulations involving MadGraph, Pythia, and Geant4.