A deeper understanding of the relationship between height and diameter (H-DBH) is crucial for enhancing forest monitoring and management, as well as for improving forest growth. Alnus glutinosa is particularly important ecologically in riparian ecosystems. However, no H-DBH models are currently available for this species. In this paper, we compared the 40 most commonly used models for predicting tree height, as published by various authors, to select the model that best predicts the height of Alnus glutinosa. We collected information on every tree in 20 randomly selected plots per population, each measuring 30 x 30 m, from twelve populations. The diameter at breast height and the dominant height of each tree were measured. The results showed that composite models performed better in height estimation. The five models that performed best in predicting tree height (those with the lowest AIC values) were m15 (AIC = 4473.1), m18 (AIC = 4463.9), m19 (AIC = 4425.2), m25 (AIC = 4425.2), and m37 (AIC = 4425.2). These models are recommended for predicting tree height in Alnus glutinosa plantations with a DBH range of 6.68–68.43 cm, provided that DBH data are available for each tree, as well as QMD and mean height (H). The analysis showed that reciprocal-transformed models, especially H=1.3+e^{(a+(b/DBH+1))}, H=a/(b+e^-c×DBH), and H=2/(a+b×DBH+c×DBH²) worked best, with these models doing better than logistic, polynomial, and exponential forms. The parameters of the models varied according to the model. These results help us to select models for forest inventory systems and emphasize the importance of using transformations.