Monitoring and assessing vegetation is essential for supporting environmental sustainability and combating desertification, particularly in regions undergoing rapid ecological transformation such as the Kingdom of Saudi Arabia. This research integrates remote sensing data (Sentinel-2 and Landsat-8) with vegetation indices (NDVI and EVI) to analyze seasonal variations and forecast afforestation progress under the Saudi Green Initiative. Machine learning models, including Random Forest and LSTM, are employed to forecast future vegetation trends. Results show vegetation peaks in spring and declines in summer due to extreme heat. The integration of AI with satellite monitoring enhances decision-making and supports sustainable environmental planning across the Kingdom.

Keywords: Vegetation Monitoring, Remote Sensing, Machine Learning, NDVI, EVI, Afforestation, Saudi Green Initiative.