Abstract: The 81 permanent sample plots of Pinus kesiya var. Langbianensis from three Yunnan forest surveys were combined with remote sensing, and the remote sensing factors with significant correlation were selected as the alternative variables for inverse modeling of carbon stock in Pinus kesiya var. langbianensis forests after independent variable screening, and three models, K-NN, RF, and PLSR, were established to optimize the best model for remote sensing of carbon stock estimation of natural forests of Pinus kesiya var. langbianensis, and multiple prediction models were proposed to predict the carbon stock in Pinus kesiya var. langbianensis natural forests in 2030 with the carbon stock estimation. A variety of models were developed to predict the carbon stock of Pinus kesiya var. langbianensis natural forest in 2030. The results showed that the coefficient of determination (R²) of the random forest model was the highest at 0.83, the root mean square error was the smallest at 5.55, and the prediction accuracy was the highest at 83.2%. In this study, it was found that the carbon stock of Pinus kesiya var. langbianensis natural forest showed a "single peak" change with the increase of elevation, and the carbon stock was mainly concentrated in the distribution of elevation 1 500-2 000 m. According to the prediction results of the power function model and the polynomial regression model, it was shown that the accuracy of the polynomial regression model was better than that of the power function model, and the R² and RMSE of the model were 0.960 and 0.054, respectively. The prediction result of Pinus kesiya var. langbianensis forest carbon stock shows that the carbon stock of Pinus kesiya var. langbianensis forest will reach 2 961.204 t in 2030, which is 42.095 t/hm² compared with that of 2 000, and this study provides a new solution for better grasping the change law of forest carbon sink.