Micro LIBS (laser-induced breakdown spectroscopy) imaging has experienced rapid growth in recent years, enabling the generation of increasingly large and complex datasets. One of the main challenges today lies in data processing, which can be extremely complex and time-consuming. In this article, we propose a new method for automatically identifying emission lines and associated elements in LIBS spectra. This work is part of LIBS imaging data processing, aimed at interpreting averaged spectra from various significant regions of the studied sample. The proposed methodology, named ALIAS (Automated Line Identification for Atomic Spectroscopy), operates by using multiple defined coefficient to find similarities between an acquired spectrum and a theoretical one derived from a simplified plasma model. The comprehensive methodology and the sequential stages of ALIAS—including peak detection, synthetic spectrum generation, intensity threshold determination, similarity coefficient computation, decision-making, and probabilistic assessment—are meticulously detailed. ALIAS’s performance is then assessed across several real-world cases that present commonly encountered challenges. This study highlights the efficiency and robustness of the ALIAS method, facilitating a high degree of automation in data processing—an enduring bottleneck that continues to hinder the widespread adoption of LIBS-based imaging technology.