The primary difference between Structural Equation Modeling (SEM) and multiple regression lies in SEM’s ability to model complex relationships, including indirect effects and latent variables, while multiple regression focuses on direct relationships between observed variables. The main assumptions required for SEM include the correct specification of the model, multivariate normality, and sufficient sample size. SEM handles high-dimensional soil data effectively by integrating multiple observable variables, such as essential macronutrients, to estimate latent variables like soil fertility and plant nutrition. This approach makes SEM particularly useful in soil science, where it can unravel the intricate interactions among soil physical, chemical, and biological properties. Before applying SEM, researchers should ensure model specification accuracy, check for data normality, assess sample size adequacy, and verify the appropriateness of their data structure to ensure reliable results.