Rapid urbanization is having a considerable impact on various aspects of living, thereby altering the biophysical environment. This study adopted the use of remote sensing technique and geographical information system (GIS) to analyse the relationship between changing land use/land cover and land surface temperature in a rapidly urbanizing tropical city of Ibadan between 1984 and 2019. Landsat series TM, ETM+, and OLI satellite imageries of Ibadan region city for 1984, 2002, and 2019, respectively, were obtained from the US Geological Survey (USGS) Landsat series of Earth Observation satellites accessible on the Google earth engine (GEE) platform. Supervised classification was done using a random forest (RF) machine learning classifier in the GEE platform. Surface emissivity maps were obtained from the normalized difference vegetation index (NDVI) thresholds method and land cover information. The surface emissivity based on NDVI classes was used to retrieve land surface temperature (LST). The results showed an increase in urban cover from 341.72 km in 1984 to 520.58 km in 2019 with an average increase in land surface temperature from 17 °C to 38 °C, respectively. Temperature sampling in the north-south and west-east transect revealed that highly urbanized areas located at the city centre of Ibadan have the highest LST of about 38 °C. It dissipates to about 19 °C at the suburb that is less built up. A significant negative relationship exists between the health condition of vegetation (NDVI) and LST with a correlation coefficient of r = - 0.95. The study confirms the potential application of GIS and remote sensing for detecting urban growth as well as relates growth impact to LST, thereby suggesting that fitting strategies will be important for the sustainable management of the urban areas.