For mitigating climate change through carbon sequestration and for reporting, Viet Nam needs to develop biomass equations at a national scale. These equations need to be accurate and provide quantifiable uncertainty. Using data from 968 trees across five ecoregions of Viet Nam, we developed a set of models to estimate tree aboveground biomass (AGB) in evergreen broadleaf forests (EBLF) at the national level. Diameter at breast height (DBH), tree height (H), wood density (WD), and combination of these three tree characteristics were used as covariates of the biomass models. Effect of ecoregion, wood density, plant family on AGB were examined. Best models were selected based on AIC, Adjusted R², and visual interpretation of model diagnostics. Cross-validation statistics of percent bias, root mean square percentage error (RMSPE), and mean absolute percent error (MAPE) were computed by randomly splitting data 200 times into model development (80%) and validation (20%) datasets and averaging over the 200 realizations. Effects models were used, the best results were obtained by using a combined variable (DBH²HWD (kg) = (DBH (cm)/100)² × H (m) × WD (g/cm³) × 1000) model AGB = a × (DBH²HWD)^b. Including a categorical WD variable as a random effect reduced AIC, percent bias, RMSPE, MAPE of models AGB = a × DBH^b and AGB = a × (DBH²H)^b; ecoregion as a random effect reduced the AIC of models AGB = DBH^b × WD, AGB = a × (DBH²H)^b, and AGB = a × (DBH²HWD)^b. For models that did not include WD variable, including plant family as a random effect reduced AIC, RMSE, and MAPE; recommendations are provided for models with specific parameters for main families and without WD if this variable is not available. The overall best model for estimating AGB was the equation form AGB = a × (DBH²HWD)^b with ecoregion as a random effect.