Machine learning (ML) has emerged as a transformative tool in the realm of laser-induced breakdown spectroscopy (LIBS), reshaping the way we can interpret and utilize the data for real-time decision-making. Since the invention of lasers and subsequently focusing it through a convex lens to produce a spectrum, LIBS has promised insights into both elemental identification and analysis. Yet, the stochastic nature of laser–matter interactions presented challenges in data processing, which was unlike the traditional absorption and emission spectra analyses. The evolution from calibration-based to calibration-free LIBS techniques marked significant advancements in usability. However, the true revolution came with the integration of ML methodologies, enabling enhanced data interpretation, accurate prediction of elemental concentrations, and material classification. This chapter provides a comprehensive introduction to ML, illustrating its profound impact and potential applications in harnessing LIBS data beyond conventional boundaries.