Land surface temperature (LST) is crucial in surface energy balance, urban climatology, intensifying global change, ecological and environmental concerns. Present study examined the LST trends and spatio-temporal variation over India for the years 2002–2022. This includes comparison of LST for summer and winter season over two decades. Secondly, study examined the LULC category wise LST variability during day and night-time using MODIS (The Terra Moderate Resolution Imaging Spectroradiometer) derived products. This study explored the feasibility of cloud computing for big data analysis for LST distribution in seven landuse categories over India, providing a conceptual response to global warming. Results showed the existence of spatial LST variation due to changes in land-use patterns and MODIS derived vegetation indices- NDVI. Daytime LST for the summer and winter period of 2002 was found to be 45.17 °C and 39.13 °C, respectively. Outcomes illustrate declining trends in range (LSTmin-LSTmax) for winter seasons, initially it was it was observed as 56.29 °C for 2002 while later on it was observed to be 20.21 °C and 20.87 °C for 2021 and 2022 respectively. The LSTmin (summer) has shown an increasing trend towards upper LST values, from −3.01 °C to 12.21 °C from 2002 TO 2022. LSTmin_winter has shown a rising trend towards the upper LST values from −17.16 °C to 9.15 °C in 2022. The maximum LST for the DRs was observed to be 61.56 °C, followed by UR as 56.24 °C. The findings demonstrate that daytime LSTmin and LSTmax are found to be 19.50 °C to 56.24 °C for UR, 29.5 °C to 61.56 °C for DR, 19.24 °C to 54.08 °C for SAR, −21.1 °C to 0.05 °C for SCR and 15 °C to 32.18 °C for FHR. NDVI-LST (daytime, nighttime and diurnal temperature range) feaure space generates an obtuse triangle and depicts negative correlation of vegetation for few LULC categories. The outcomes indicated that desert and snow regions have highest LSTmax followed by urban and semiarid regions during day. During nighttime, desert and urban regions have highest temperature followed by semi-arid and forest regions. The outcomes support the efficacy of earth observation datasets and help to facilitate a better understanding of LULC and its impact on regional climate changes.