Abstract: A critical challenge in environmental health research is the comprehensive assessment of health effects associated with exposure to multiple environmental pollutants. Traditional single-pollutant models are inadequate for exploring highly correlated, interactive, and non-linear relationships. Therefore, more advanced mixed effects models have been developed, including weighted quantile sum regression (WQS), quantile g-computation, Bayesian kernel machine regression (BKMR), and margins. WQS model addresses collinearity by constructing a single composite index, but assuming that all pollutants act in the same direction. Quantile g-computation relaxes this assumption, offering greater flexibility for mixed-direction effects. BKMR, employing a Bayesian nonparametric approach, further transcends traditional parametric assumptions, effectively capturing complex non-linear and interactive relationships between pollutants. Margins analysis provides intuitive scenario-based interpretations to clarify policy implications. This paper systematically outlines the evolutionary trajectory of the four mixed exposure models, from linear simplifications to non-linear interactions. It is also to emphasize the bridging role of marginal effect analysis in translating statistical model result into concrete policy scenarios.