The present study aims at enhancing the detection process in utilization of satellite image analysis based on the Normalized Difference Vegetation Index (NDVI). The aforementioned technique is one among the pioneers of remote sensing analytical process that could simplify the multi-spectral data. NDVI is the highly preferred indexing method for the assessment of vegetation and analysis of the vegetation change detection. The ability to compute an NDVI using any multispectral sensor with a visible range and near-IR range has led to its popularity and widespread use of Landsat TM image Remote Sensing data. In the present research satellite image processing using NDVI differencing was employed for the vegetation change analysis. Remote Sensing data provides information on parameters that helps in prioritization of vegetation like size and area of the vegetation. A variation with the NDVI was performed for the year 1990 and 2020. It includes the comparison of annual average NDVI between the year 1990 and 2020. Here, the greenish yellow pixels indicate a bigger amount of vegetation space. The variation in vegetation analysis is an economical way for recording the changes discovered in every land use class. Over a decade, there have been considerable variations in vegetation is observed from agricultural land, mountain ranges, and in dry farming areas. NDVI threshold values recorded as 0.22 for Vegetation. Thus NDVI is highly effective for recording surface characteristics in the visible region, which aids policymakers in making firm developmental plans.