A pilot-scale hybrid constructed wetland with vertical flow and horizontal flow in series was constructed and used to investigate organic material and nutrient removal rate constants for wastewater treatment and establish a practical predictive model for use. For this purpose, the performance of multiple parameters was statistically evaluated during the process and predictive models were suggested. The measurement of the kinetic rate constant was based on the use of the first-order derivation and Monod kinetic derivation (Monod) paired with a plug flow reactor (PFR) and a continuously stirred tank reactor (CSTR). Both the Lindeman, Merenda, and Gold (LMG) analysis and Bayesian model averaging (BMA) method were employed for identifying the relative importance of variables and their optimal multiple regression (MR). The results showed that the first-order–PFR (M₂) model did not fit the data (P > 0.05, and R² < 0.5), whereas the first-order–CSTR (M₁) model for the chemical oxygen demand (COD_Cr) and Monod–CSTR (M₃) model for the COD_Cr and ammonium nitrogen (NH₄−N) showed a high correlation with the experimental data (R² > 0.5). The pollutant removal rates in the case of M₁ were 0.19 m/d (COD_Cr) and those for M₃ were 25.2 g/m²∙d for COD_Cr and 2.63 g/m²∙d for NH₄-N. By applying a multi-variable linear regression method, the optimal empirical models were established for predicting the final effluent concentration of five days' biochemical oxygen demand (BOD₅) and NH₄-N. In general, the hydraulic loading rate was considered an important variable having a high value of relative importance, which appeared in all the optimal predictive models.