Land Use and Land Cover (LULC) change refer to the loss of natural areas, particularly forests, agricultural areas, or water bodies, to urban or exurban development. Understanding how LULC will impact the district of Hanamkonda’s water resource availability is crucial. In order to conduct tasks like change detection analysis and theme mapping, baseline data on land cover must be determined. Expanding urban areas affects natural resources and makes them vulnerable. As it is observed that rapid changes are occurring in LULC around the water bodies, this will badly affect the quantity and quality of water resources, increasing the pressure on water availability in urban areas. It also creates flood hazards in the surrounding areas of the water bodies due to not protecting the boundaries of the water bodies. Any loss in the water surface area will also impact the groundwater resources in the region. The Hanamkonda district of Telangana state, India, has many water bodies. Over the period, the surroundings of some of the water bodies are highly urbanised, causing stress on water resource availability and flood-related problems during monsoon season. The land use and land cover changes for the four lake systems in the Hanamkonda district over a ten-year period, from 2013 to 2022, are presented in this paper using machine learning algorithms in the Google Earth Engine site. The accuracy assessment is used to compare the performance of the two machine learning algorithms such as Random Forest (RF) and Support Vector Machine (SVM) in the classification of LULC. For the years 2013, 2016, 2019, and 2022, Landsat-8 data is used, and the major LULC classes are ‘water bodies’, ‘urban’, ‘vegetation’, and ‘barren’. The average overall accuracy of RF and SVM classifiers is 88.47% and 91.92%, respectively. The results suggest that the support vector machine classifier outperforms the random forest classifier in terms of accuracy. The findings revealed that from 2013 to 2022, water bodies (− 2.387 km2) had a decreasing trend, whereas urban areas (1.4925 km2), vegetation (0.022 km2), and barren areas (0.874 km2) had an increasing trend. The urban area for the Bhadrakali, Dharmasagar, and Waddepally lake systems has seen an almost 50% increase. The water bodies are mostly affected for Dharmasagar and Chinna Vaddepalli lake systems. This study helps to analyse the lake systems and is used for better management of water resources.