Abstract: Suitable Normalized Difference Vegetation Index (NDVI) prediction models were developed for the Xizang region using AutoRegressive Integrated Moving Average (ARIMA), Random Forest (RF), and prophet methods. The validation was conducted using MODIS remote sensing NDVI data for the Yangbajing area from 2000 to 2021. The results show no significant decrease in the overall vegetation coverage in this region. Among the three prediction models, RF demonstrates the highest prediction accuracy, with normalized root-mean-squared error, mean absolute percentage error, and coefficient of determination of 6.92%, 4.04%, and 0.9, respectively. The wavelet transformation method efficiently enhances the prediction accuracy of the models. The combined models improve prediction accuracy, and the reciprocal of error weights combined model outperforms the average weight and inverse variance weighted combined models. Therefore, machine learning methods such as RF, when combined with wavelet transformation and the reciprocal of error weights model, can be effectively utilized for NDVI prediction in the Xizang region. This approach provides scientific guidance for ecological protection and agricultural decision-making.