Machine learning models appear to be an attractive route towards tackling high-dimensional problems, particularly in areas where a lack of knowledge exists regarding the development of effective algorithms, and where programs must dynamically adapt to changing conditions. The objective of this study was to evaluate the performance of three machine learning tools for predicting stand volume of fast-growing forest plantations, based on statistical vegetation metrics extracted from an Airborne Laser Scanning (ALS) survey. The forests used in this study were composed of 1138 ha of commercial plantations that consisted of hybrids of Eucalyptus grandis and Eucalyptus urophylla, managed for pulp production. Three machine learning tools were implemented: neural network (NN), random forest (RF) and support vector regression (SV); and their performance was compared to a regression model (RM). The RF and the RM presented an RMSE in the leave-one-out cross-validation of 31.80 and 30.56 m³ ha⁻¹ respectively. The NN and SV presented a higher RMSE than the others, equal to 64.44 and 65.30 m³ ha⁻¹. The coefficient of determination and bias were similar to all modeling techniques. The ranking of ALS metrics based on their relative importance for the estimation of stand volume showed some differences. Rather than being limited to a subset of predictor variables, machine learning techniques explored the complete metrics set, looking for patterns between them and the dependent variable.