Plantation forests provide various benefits, including wood production and ecosystem services, which vary across plantation types. Accurate biomass estimation is crucial for assessing carbon stocks and sustainable management. This study focused on mixed plantations to estimate root biomass and develop allometric equations for E. camaldulensis and G. robusta in Guangua District, Northwest Ethiopia. Through destructive sampling and statistical analysis, species- and site-specific equations were carriedout. A total of 72 trees were harvested across three plantation types 100% E. camaldulensis, 100% G. robusta, and mixed plantations, with 24 trees per type. Key measurements included DBH, height, wood density, aboveground biomass, and root biomass. Root biomass was collected using the Voronoi polygon method and estimated through excavation, while regression analysis was employed to develop biomass and height models.Thirteen biomass prediction models and seven DBH-height models were assessed using AIC, adjusted R², RMSE, bias, and t-tests. Pure E. camaldulensis and mixed plantations exhibited higher root biomass than pure G. robusta. DBH was the best predictor of root biomass across all treatments in a power function form, while in mixed plantations, DBH and height were optimal predictor in logarithmic form. For aboveground biomass, DBH and height were the best predictors with power function form, whereas a logarithmic model incorporating DBH, height, and wood density performed best for mixed plantations. Total biomass followed similar trends, with site-specific models outperforming regional ones. For height prediction using DBH, the best model across all treatments with a logarithmic form, while a polynomial model was optimal for mixed plantations. The developed equations enhance biomass estimation accuracy, aiding carbon stock assessment and sustainable forest management. These models support policymakers and forest managers in optimizing plantation strategies and improving carbon sequestration efforts.