Description of the subject. This study evaluates the application of Boosted Regression Trees (BRT) for predicting beech dominant height in the Hyrcanian forests of Iran, inscribed as a UNESCO’s World Heritage due to its remarkable biodiversity. Objectives. It is widely accepted that tree growth can be influenced by a wide variety of factors such as climate, topography, soil conditions and competition for resources. The early dominant height of trees modelling studies used the multiple linear regression. The development of more advanced non-parametric and machine learning methods provided opportunities to overcome the nonlinear relationships in forest ecosystems. Method. In this study, boosted regression trees was evaluated to model the dominant height of Fagus orientalis as the most important tree species in the Hyrcanian forest, Iran. Dominant height was related to soil and topographical variables, which are available for 190 sample plots covering all importance environmental gradients in the research area. Results. The results indicated BRT were found to outperform for modelling beech dominant height. This technique showed that phosphorus, percentage nitrogen, magnesium and percentage sand were among the most important variables. Conclusions. This study demonstrates the ability of BRT to accurately model the dominant height of oriental beech in relation to environmental predictors, and encourages its use in forest ecology.