The surface roughness aerodynamic parameters z₀ (roughness length) and d (zero-plane displacement height) are vital to the accuracy of the Monin–Obukhov similarity theory. Deriving improved urban canopy parameterization (UCP) schemes within the conventional framework remains mathematically challenging. The current study explores the potential of a machine-learning (ML) algorithm, a random forest (RF), as a complement to the traditional UCP schemes. Using large-eddy simulation and ensemble sampling, in combination with nonlinear least squares regression of the logarithmic-layer wind profiles, a dataset of approximately 4.5 × 10³ samples is established for the aerodynamic parameters and the morphometric statistics, enabling the training of the ML model. While the prediction for d is not as good as the UCP after Kanda et al., the performance for z₀ is notable. The RF algorithm also categorizes z₀ and d with an exceptional performance score: the overall bell-shaped distributions are well predicted, and the ±0.5σ category (i.e., the 38% percentile) is competently captured (37.8% for z₀ and 36.5% for d). Among the morphometric features, the mean and maximum building heights (H_ave and H_max, respectively) are found to be of predominant influence on the prediction of z₀ and d. A perhaps counterintuitive result is the considerably less striking importance of the building-height variability. Possible reasons are discussed. The feature importance scores could be useful for identifying the contributing factors to the surface aerodynamic characteristics. The results may shed some light on the development of ML-based UCP for mesoscale modeling.