Long-term change detection using remote sensing imagery and geographic information system (GIS) is playing a dynamic role for extracting information about the land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and land use land cover (LULC). Therefore, this study was aimed to focus on the change detection of LULC, and investigated the relationship between LST, NDVI, and NDWI over Sankrail Block of Jhargram District, West Bengal using the Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) imageries in the year of 2000 and 2020. The unsupervised Iterative Self-Organizing Data Analysis Technique (ISODATA) classification algorithm was applied to classify the LULC classes such as water body, vegetation, agriculture, sand, settlement, and fallow land over this study area. The result indicated that the correlation between LST and NDVI was showed a positive correlation of R2 = 0.23 in 2000 and R2 = 0.12 in 2020, respectively. In case of LST and NDWI, the result presented a positive result of R2 = 0.12 in 2000 and R2 = 0.37 in 2020, respectively. The classification-based result was revealed that the vegetation cover, water body, settlement, and fallow land have been increased by 20.02, 0.25, 8.13 and 2.22 Sq.km, while agriculture and sandy area were decreased by 29.97 and 0.67 Sq.km due to anthropogenic activities, respectively during 2000–2020. The overall accuracy was estimated as 80.00% in 2000 and 89.00% in 2020, where the kappa coefficient was observed as 0.89 in 2000 and 0.89 in 2020. The findings of this study also showed that the LST has a significant impact on the terrain surface and LULC class. As a result, the findings of this study may aid planners and decision-makers in effectively guiding the sustainable land development of areas with similar backgrounds.