Forest growing-stock (GSV) measurements at the national level are laborious and costly; however, integrating satellite data and machine learning (ML) methods provides an appreciable approach with great prospects. Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) were used to predict GSV using Estonian NFI data, Sentinel-2 imagery, and ALS point-cloud data. Four data scenarios were tested: vegetation indices and LiDAR (CO1), vegetation indices and individual band reflectance (CO2), LiDAR and individual band reflectance (CO3), and a combination of vegetation indices, individual band reflectance, and LiDAR (CO4). Comparatively, across Estonia’s geographical regions, RF consistently outperforms other performance models. In the northwest (NW), RF achieved the best performance with the CO3 combination, with an R2 of 0.63 and an RMSE of 125.39 m3/plot. In the southwest (SW), it yielded an R2 of 0.73 and an RMSE of 128.86 m3/plot with the CO4 variable combination. The RF performance in the northeast (NE) resulted in an R2 of 0.64 and an RMSE of 133.77 m3/plot under the CO4 combination. Finally, in the southeast (SE) region, the best performance was achieved with the CO4 combination, yielding an R2 of 0.70 and an RMSE of 120.56 m³/plot. These results underscore RF’s precision in predicting GSV across diverse environments, though refining variable selection and improving tree species data could further enhance accuracy.