[Objective] A factor importance analysis of vegetation changes in East Africa based on different machine learning algorithms was conducted to measure the accuracy and applicability of the different algorithms in order to provide a scientific basis for protecting, restoring, and promoting sustainable forest management and comprehensive prevention and control of soil erosion. [Methods] Changes in normalized difference vegetation index (NDVI) for nine countries in East Africa from 2001 to 2020 were determined. The independent treatment variables were two climatic factors and five human activity factors affecting vegetation changes in East Africa. Six machine learning algorithms were used to establish NDVI prediction models:random forest (RF), BP neural networks (BP), support vector machines (SVM), genetic algorithm (GA), radial basis function (RBF), and convolutional neural networks (CNN). Coefficient of determination (R²), mean absolute error (MAE), and mean relative error (MRE) were used as error indicators to evaluate the potential of the six machine learning algorithms for predicting NDVI changes. Based on the optimal model (RF), the importance of the selected seven factors was determined. [Results] The accuracy verification results showed that the regression accuracy of the CNN algorithm was the worst for the full factor case in the study area. After deleting an algorithm with poor comprehensive performance in each round of testing, the model established by the RF algorithm had the highest regression accuracy for NDVI change analysis in East Africa. The importance of different factor variables to NDVI change based on RF showed that annual precipitation, N₂O emission, CH₄ emission, and livestock number had the greatest influence on the results of the NDVI change regression. [Conclusion] RF had a comparative advantage for NDVI simulation in East Africa. Precipitation was the most important climatic factor affecting vegetation changes. At the same time, greenhouse gas emissions also had an important impact on vegetation changes in East Africa. East African countries should raise awareness and understanding of the interdependence of vegetation changes on climate, environment, socio-economic conditions, and political systems, and develop appropriate policies to promote sustainable forest management and to combat desertification.