Wildfires are today’s one of the most significant environmental issues since they disrupt ecosystems and have an adverse effect on the environment, the economy, and society. Severe post-fire consequences are a result of an increasing frequency of wildfires worldwide. Post-fire management depends on knowing the precise distribution of wildfire burn intensity. The Landsat-8 from UGSS and NASA offers a great tool for the identification, mapping, and monitoring of fire impacts. The main objective of this study is to evaluate the potential of Landsat-8 for mapping fires in forest regions that also have vegetative land within them and to monitor the responses from the forest and vegetation. To detect the forest fire, spectral indices such as NDVI, GNDVI, NBR, RBR, and RdNBR, were utilized through Google Earth Engine. The mapping of the forest land was done through the random forest classifier. To check the variability in the temperature of the region at pre- and post-fire events, land surface temperature maps of the area were generated. The results suggest that the forest fire map generated through RdNBR is much more reliable in the present study. Higher RdNBR values fall within the forest region as verified through the classified and LST maps of the region. The comparison between different vegetation indices has been used for the detection of forest fire, in which the RdNBR index produces the most consistent fire map of the Panna, M.P. region due to the presence of the vegetation (agricultural land) within the forest land.