Knowledge about the production of a forest area is essential for planning management activities, and machine learning techniques applied to remote sensing data have contributed to obtaining indirect production estimates at a reduced cost. The objective of this work was to evaluate the use of the Random Forest Regressor (RFR) algorithm in estimating the volume of Pinus taeda L. by analyzing hyperspectral data from the EnMAP orbital sensor. The model adjustment results were considered satisfactory on the test data, where the coefficient of determination (R2) was 0.586, the root mean squared error (RMSE) was 69.77 m3.ha-1 and the mean squared error (MAE) was 53.75 m3/ha. The spectral band groups that best explain the model are located within the near-infrared (NIR: ~734.65 nm to ~778.56 nm) and shortwave-infrared (SWIR: ~1780.22 nm to 1986.45 nm) regions. Finally, a production volume prediction map (m3/ha) was generated and compared with the average volume (m3/ha) of the inventory. There was no statistically significant difference between the inventory data and the volume data predicted by the map, confirming that the RFR has potential application in estimating the volume of Pinus taeda L. in southern Brazil.