One of the most effective approaches to mitigating climate change is to monitor the carbon stock in the tropical rainforests. However, biomonitoring of carbon in the forests is expensive and challenging due to inaccessibility. To improve carbon stock monitoring and the evaluation of fine-scale forest loss, we established a rapid, automatic, and cost-efficient generalized machine learning framework that uses diverse remote sensing data and satellite imagery to accurately estimate aboveground carbon density, at fine-grained resolution (tens of meters), in remote tropical rainforests. The study area first focused on rainforests in Indonesian Borneo. In our preliminary tests on 80 sites in Indonesian Borneo, our machine learning models were capable of producing ACD estimates with R2 of 0.7-0.8, which is a significant improvement from the comparable works (0.5-0.6 at best). This machine learning framework will be used to facilitate further carbon stock modeling in other forest regions (e.g. Brazil) as well as for the general purpose of climate change mitigation.