Allometric equations are fundamental tools in ecological research and forestry management, widely used for estimating above-ground biomass and production, serving as the core foundations of dynamic vegetation models. Using global datasets from Tallo—a tree allometry and crown architecture database encompassing thousands of species—and TRY—a plant traits database—we fit Bayesian hierarchical models with three alternative functional forms (power-law, generalized Michaelis–Menten (gMM), and Weibull) to characterize how diameter at breast height (DBH), tree height (H), and crown radius (CR) scale with and without wood density as a species-level predictor. Our analysis revealed that the saturating Weibull function best captured the relationship between tree height and DBH in both functional groups, while the CR-DBH relationship was best predicted by a power-law function in angiosperms and the gMM function in gymnosperms. Although including wood density did not significantly improve predictive performance, it revealed important ecological trade-offs: lighter-wood angiosperms achieve taller mature heights more rapidly, and denser wood promotes wider crown expansion across clades. We also found that accurately estimating DBH required considering both height and crown size, highlighting how these variables together distinguish trees of similar height but differing trunk diameters. Our results emphasize the importance of applying saturating functions for large trees to improve forest biomass estimates and show that wood density, while not always predictive at broad scales, illuminates the biomechanical and ecological constraints underlying diverse tree architectures. These findings offer practical pathways for integrating height- and crown-based metrics into existing carbon monitoring programs worldwide.