Rapid urbanization and environmental degradation pose major problems for the Hyderabad city in India. Knowing how much land use and land cover (LULC) is changing is necessary to ascertain the adverse effects urban development continue to have on the environment. Urbanization has greatly influenced changes in natural landscapes through increased construction activities, vehicular emissions as well as industrial growth. The researchers used Sentinel-2 imagery based remote sensing data processing technology to analyze LULC dynamics over a period of three years: 2019, 2022 and 2024. In order to monitor various types of land cover like vegetation, water bodies, barren lands, and built-up areas, the researchers relied on high-resolution multispectral imagery retrieved from Sentinel-2 satellite data. This research provides an overview of urban sprawl patterns that have emerged over the last five years including some of their environmental consequences such as loss of green cover or increased extent of impermeable surfaces. Random Forest is one of those employed machine learning algorithms that achieved highest values for 2019 (95.23%), 2022 (96.23%) and 96.24% in case of LULC classification and change detection showing its superiority among other methods used. The accuracies for CART in the same years were 95.01%, 93.65%, and 92.9% respectively which were closely related to those of Random Forests’ accuracy. In comparison, Naive Bayes gave an insight on the data with an accuracy of 62.06, 92.06, and 83.56%. The findings show observable trends at first glance: the changing of agricultural land into constructed environments thus it is apparent that there is a need for sustainable urban planning immediately.