Laser-induced breakdown spectroscopy (LIBS) remains widely utilized in various scientific and industrial applications for its fast, in situ, quasi-nondestructive analysis of materials. However, the size and complexity of the datasets generated through LIBS necessitate efficient approaches for their handling and interpretation. This chapter examines the application of artificial intelligence (AI) techniques to manage large volumes of multidimensional LIBS data and enhance analytical processes, including data analysis and interpretation while highlighting its role in improving real-time analysis and decision-making. Focusing on waste-to-energy applications, this chapter demonstrates how AI models, including support vector regression (SVR) and random forest (RF), streamline the prediction of critical waste properties such as proximate and ash elemental analyses and heating values. These models and cross-validation techniques ensure high generalization ability for LIBS qualitative/quantitative analyses, demonstrating the transformative potential of AI in optimizing industrial workflows and driving sustainable technological innovations.