Land surface temperature information is relevant to many earths’ scientific topics and problems, including human-environment interaction, global environmental change, and more especially urban climatology. Therefore, the purpose of this study was to analyze the spatio temporal changes in land surface temperature Dynamics in response to in land use and land cover change within the Jawi woreda. The researcher used geospatial technology and multispectral, multitemporal Landsat data (TM, OLI and TIRS) as a secondary data source. The study used the emissivity-corrected LST method to extract and analyze LST and created land use and land cover (LULC) maps for the period from 1988 to 2022 using random forest machine learning algorithms in Google Earth Engine (GEE). In addition, the researcher used ArcMap 10.8.2 software to calculate the spatial coverage of LST and applied zonal statistics as a table to illustrate the relationship between LST and each LULC class. The findings demonstrate a consistent rise in mean LST over time, with the highest amount recorded in 2022, from 28.78˚C in 1988 to 33.61˚C in 2022. The LST accuracy of the study area has been verified by comparing LST meteorological data obtained from various weather stations with the findings of Landsat TM and OLI/TIRS. The mean temperature increased between 27.41°C and 33.33°C and 28.79°C and 33.62°C, respectively. As a result, the mean temperature results for the two datasets were similar. The normalized difference vegetation index (NDVI) and LST have a strong negative correlation, with statistically significant p-values of <0.005. The study classifies the study area into five major classes (Water body, cropland, settlement, forestland, and shrub/bush), depicting changes such as a decline in water bodies from 2.42% to 0.47% and an increase in cropland from 4.75% to 11.81%. Forestland sees a rise from 29.98% to 39.52%, while shrub/bushland decreases from 62.46% to 47.60%. Moreover, Google Earth and handheld Garmin GPS were employed for LULC classification validation, with the highest accuracy achieved in the 2022 classification (94%). However, the analysis of distributed LST changes within each LULC class shows that, from 1988 to 2022, the mean LST was reported for increases in shrub/bushland (21.9 to 29.4˚C), settlement (23.6 to 32.8˚C), and farmland (26 to 35˚C). As a result, this study endeavor aimed to provide valuable insights to support well informed decision makers ensuring the preservation of biodiversity, unique ecosystems, climate change, and the overall health of the study area. Keywords: Google Earth Engine, Jawi, LULC, Land surface temperature, Random Forest