This chapter explores the integration of machine learning (ML) with Laser-Induced Breakdown Spectroscopy (LIBS), a powerful technique for elemental analysis. The combination of ML and LIBS enhances data processing, enabling more precise material classification, elemental concentration predictions, and complex dataset interpretation. The chapter begins by introducing the fundamental concepts of machine learning, including supervised, unsupervised, and deep learning methods, and their applications within the context of LIBS. Key supervised machine learning techniques such as decision trees, random forests, support vector machines, and neural networks are analyzed, highlighting their roles in improving LIBS accuracy and efficiency. The chapter also emphasizes the importance of data splitting and feature selection to optimize model performance. Special attention is given to advanced methods like Gaussian process regression and self-organizing maps, which offer probabilistic and unsupervised approaches to spectral data analysis. The chapter concludes by addressing the ongoing challenges in applying machine learning to LIBS, including computational demands and model interpretability, while outlining the potential future advancements that will further enhance the analytical capabilities of LIBS.