Bamboo forms an essential and integral component of the natural landscape as well as the socio-cultural and economic lives of the people of Northeast India. India is home to nearly 45% of global bamboo patches and Northeastern Region (NER) comprises of two-thirds of the total growing stock of the country. However, there has been a lot of challenges and loopholes identified in terms of availability of consistent and accurate data on different aspects of bamboo such as area coverage, productivity, carbon sequestration, feedstock, consumption & livelihood. Earth observation data has been proved to be highly instrumental and successful towards spatial mapping of natural resources, including bamboo ecosystems. The present study aimed at delineating & analyzing the bamboo groves of Karbi-Anglong district of Assam using geospatial technologies. Random Forest Classifier technique was adopted for supervised classification in Google Earth Engine (GEE) using the necessary scripts and codes. Bamboo Index (BI) for the study area was computed by incorporating Normalized Difference Vegetation Index (NDVI) and Stress Index (SI). Results of the research work revealed that bamboo patches in the study area had BI values ranging from 0.42 to 0.49. Furthermore, majority of the area was observed to be occupied by the forests (Tropical Moist Deciduous Forests & Tropical Semi-Evergreen Forests) followed by Bamboo groves, Agriculture, Fallow Land, Barren Land, Waterbodies and Built-up areas. The findings were instrumental in contributing to the ambit of information on the Bamboo Groves of Karbi-Anglong District, Assam. This could prove to be vital in creation of a reliable database for monitoring and management of one of the most essential renewable Non-Timber Forest Resources considered as ‘green gold’ and ‘poor man’s timber’ thereby contributing towards elimination of few of the gaps and challenges which has direct implications on the growth and development of the bamboo sector in Northeastern region.