Harnessing Machine Learning and Deep Learning Approaches for Laser‐Induced Breakdown Spectroscopy Data Analysis: A Comprehensive Review

Laser‐induced breakdown spectroscopy (LIBS) is a rapid, accurate technique for material analysis, offering real‐time, minimally destructive, and in situ detection capabilities with broad application potential. LIBS extends its applications across various fields, from geology to biomedicine. However, barriers like matrix effects, reproducibility, self‐absorption, and spectral noise often restrict the proper interpretation of the spectra. This review paper examines literature from 2015 to 2025, focusing on the evolution of machine learning (ML) and deep learning (DL) techniques, in LIBS analysis. It evaluates the advancement of these techniques, assessing both the qualitative and quantitative performance of LIBS analysis. These observations support the complementary roles of ML and DL methodologies. ML captures general patterns, while DL, through convolutional neural networks (CNNs), excels at identifying high‐level features. This literature review reveals that no single ML or DL tool consistently provides optimal solutions for LIBS applications. The analysis pipeline needs to be tailored based on the LIBS data and the goal of the study. Designing such a framework requires the incorporation of preprocessing techniques to enhance the quality of raw signals. This step should then be followed by integrating the data into predictive models, whether ML or DL, to accomplish tasks like classification or concentration prediction.