Abstract: The marginal effect (margins) model, a regression-based approach for estimating marginal effects, has emerged as a promising tool for assessing complex interaction structures in environmental health studies in recent years. This model is particularly suited for uncovering the direction and magnitude of multivariable interaction structures under complex combined exposures. This review systematically evaluates the theoretical foundations and practical applications of the margins model in environmental epidemiology. Drawing on case studies involving heavy metal mixtures, we illustrate its applicability, modeling strategies, and interpretative advantages across different health outcomes. Compared to traditional regressions with interaction terms, the margins model quantitatively characterizes higher-order interactions involving three or more variables while controlling covariates, and provides explicit directional inference. In contrast to nonparametric models such as Bayesian kernel machine regression and weighted quantile sum regression, the margins model offers superior scalability for modeling high-order interactions and explaining subgroup stratifications. Although it has limitations in nonlinear modeling, high-dimensional variable selection, and visualization, the integration of margins model with Bayesian method, penalized regression, and machine learning is expanding its theoretical scope and application potential. We conclude that the margins model holds significant promise as a core analytical tool for disentangling key interaction structures, supporting causal inference, and facilitating risk stratification in future research with multi-exposure multi-outcome frameworks.