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With an escalatory growth of human population in the 21st century there has been a significant increase in demand of water for human survival. With the limitation of Surface water sources now ground water that contributes to the total annual supply of water consumption and outshines as one of the major sources. The objective of this paper is to review and analyze the area of Gautam Buddh Nagar, Uttar Pradesh using remote sensing and Geographic Information System for identification of groundwater potential zones. There is various expertise which are used for different water zones for its mapping and its analysis. Parameters like density, soil, land use\cover, geology, drainage, rainfall, contour and terrain are used for controlling groundwater zones. These Groundwater mapping methods are explained and extracted from data sources of satellite images. These techniques include methods which are advance and conventional. For identification and mapping of groundwater potential analysis the thematic layers are used. The significance of each layer is discussed for the location groundwater potential zones using the conditions present. For effective exploration and identification of appropriate locations for water extraction this groundwater potential information will be helpful. For analysis exploration and study of a location remote sensing data is a fast, cost-effective and economical way of doing it. With the Integration of these data for the exploration of groundwater resources has been seen as a breakthrough in the field of its research, and plays a significant role for providing assistance in monitoring, accessing, and preservation of groundwater resources. In the present paper, the assessment of groundwater availability in Gautam Buddha Nagar, Uttar Pradesh, India have been conducted using these techniques. Various maps were prepared like base map, DEM, drainage density map, contour map, land use map, lineament density map and groundwater potential zones using the data of remote sensing and the existing maps. Raster data was transformed from base map and DEM using feature to raster converter tool in ArcGIS 10.3 version

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