Forest inventory and monitoring is normally carried out based on field measurements of biophysical attributes such as diameter, height, and the number of trees, which is a labor, time, and money consuming activity. Satellite data associated with artificial intelligence tools are alternative approaches to estimate forest parameters at large scales. We assessed correlation of forest variables measured in the field with different vegetation indices (VIs) (normalized difference vegetation index, soil adjusted vegetation index, modified soil adjusted vegetation index, enhanced vegetation index, and enhanced vegetation index adjusted 2.2), retrieved from Sentinel-2 imagery to predict the volume of commercial trees (VCC) showing a minimum commercial diameter   (  MCD  )    ≥  50  cm in a sustainable forest management plan in the Brazilian Amazon region. A total of 150 artificial neural networks (ANNs) of the multilayer perceptron type were trained and supervised. Subsequently, the five best-performing networks were retained based on the fit and accuracy statistics. The ANN-1 showed the best statistical results [root-mean-square error <10  %   and correlation coefficient   (  r  )    >  0.98] to predict the VCC using as input variables the number of trees per hectare showing MCD  ≥  50  cm and all tested VIs. Our study shows promising results that may contribute to improving forest management planning at large scales in remote areas in tropical regions.