Due to long term drought, engineered structures (e.g., dams and levees), and other stressors, river systems are at high risk of degradation. Riparian vegetation and river geomorphology are continuously changing. The change in river hydrology, geomorphology and riparian vegetation have cascading impacts on other ecological aspects of the river corridor system. In this study, spatiotemporal variations of the riparian vegetation and the river geomorphology have been characterized using machine learning techniques (in particular, random forest) over an evaluation period of three decades. The study area is the Middle Rio Grande, located in New Mexico, USA. For the study of vegetation, the normalized difference vegetation index (NDVI) was used. The land cover was classified, using Landsat images (1984 to 2020) collected from Landsat 5, 7 and 8, to determine the change in vegetation cover and river geomorphology. The trends of NDVI shows the increase in vegetation cover even during long term drought due to presence of groundwater dependent vegetation like cottonwoods. Similarly, the formation of new stable channel islands and narrowing of the channel are some major observations and changes in channel from this study. The availability of long-term datasets and machine learning algorithms in Google Earth Engine shows the potential in spatiotemporal analysis of riparian vegetation and river geomorphology. These long-term observations will help river managers to monitor the status of the riparian vegetation and the impacts on the river geomorphology.