In Ethiopia, smallholder farmers commonly grow eucalyptus plantations in their farmland and other communal areas for short-rotation harvests (4–5 years). Eucalyptus trees are rapidly expanding in the central highlands of Ethiopia and in the Amahara region. It gives socioeconomic, environmental, and ecological benefits. The expansion and socioeconomic value of Eucalyptus plantations have been studied in the study area. Hence, this study was conducted to develop a species-specific allometric model to estimate the aboveground biomass of this smallholder plantation using the destructive method. The sample design was applied using both purposive and simple random sampling techniques. A total of 60 trees with diameters at breast height ranging from 2.1 to 12.6 cm were randomly harvested and separated into tree components (stem, branch, leaf, and twigs) weighted in the field and in the laboratory for sample dry and fresh weight analysis. Model development was done using 75% of the total data set, whereas the remaining 25% was used for model validation. Eight different above-ground equations and three height prediction equations were developed and tested. Diameter at the breast height, total height, and woody density were used as predictor’s variables, whereas aboveground biomass is a dependent variable. For the height prediction model, the diameter was used as an independent variable. The best aboveground biomass models were selected based on the statically indices the highest coefficient determination adjiR 2 ,and the lowest residual standard error, Akaike information criteria, correction factor, mean absolute percentage error, and root mean square error. Species-specific models using the three simple predictor variables fitted the data in the form of: ln(Y) = -2.875 + 1.243ln (D) + 1.631ln (H) + 0.665ln (p) was selected as the best fit model for Wogera areas. This model has coefficient determination (Adji R2) (0.94), residual standard error (0.230), Akaike Information Criteria (1.297), a correction factor (1.027), a mean absolute percentage error (15.71%), and a root mean square error (2.88). Comparing the current model (M6) with the four general existing models, the current model approximately predicted the smallholder plantation better than the general existing model, followed by model lnY = -2.187+ 0.916 ln(D^2H ρ) / 0.112 (D^2H ρ) ^0.916. The best-fit model for the height prediction model was model (M3) with adjiR2 (0.84), RSE (0.102), and AIC (-78) in the form lnH = 1.400 + 0.5 (lnD). Based on the findings, it is possible to conclude that the developing species-specific models are preferable to accurately estimate aboveground biomass and height prediction allometric models compared to the existing or previously developed general models.