Tree leaf mass is a small, highly variable, but critical, component of forest ecosystems. Estimating leaf mass on standing trees with models is challenging because leaf mass varies both within and between tree species and at different locations and points in time. Typically, models for estimating tree leaf mass are species-specific, empirical models that predict intra-specific variation from stem diameter at breast height (DBH). Such models are highly limited in their application because there are many other factors beyond tree girth and species that cause leaf mass to vary and because such models provide no way to predict leaf mass for species for which data are not available. We conducted destructive sampling of 17 different species in Michigan, covering multiple life history traits and sizes, to investigate the potential for using a single, ‘trans-species’ model for predicting leaf mass for all the trees in our study. Our results show the most important predictors of tree leaf mass are DBH, five-year basal area increment, crown class, and competition index, none of which are species specific. Species-specific variation could be captured by leaf longevity and shade tolerance and wood specific gravity was a statistically significant, but marginally-important predictor. Together, these variables describing tree size, life-history traits, and competitive environment were able to allow us to develop a generalized leaf mass model applicable to a diverse set of species, without having to develop species-specific equations.