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Structural Equation Modeling (SEM) is a crucial analytical instrument in business and social sciences, allowing researchers to examine intricate correlations between observable and latent variables while reducing measurement errors. This study conducts a systematic literature review (SLR) of SEM-related research published in the ABAC Journal from 2021 to 2024. The review classifies research articles according to their application domains, estimating techniques, software utilization, theoretical frameworks, and geographical study sites. Research indicates that SEM is mostly employed in marketing, management, travel and tourism, finance, human resource management, education, accounting, and economics. Maximum Likelihood Estimation (MLE) is the predominant method, followed by Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Integrated Generalized Structured Component Analysis (IGSCA). AMOS and SmartPLS are identified as the favored SEM software. The research underscores the growing utilization of hybrid SEM methodologies, which combine factor-based and component-based models to enhance analytical flexibility. These findings offer significant insights for researchers and practitioners, assisting them in choosing suitable SEM approaches according to research aims and data attributes. Future research should investigate the use of IGSCA and conduct comparative assessments of various SEM estimate techniques across varied data situations to improve methodological progress in business and social science research.