The global issue of urban heat island (UHI) formation affects both developed and developing nations. Climate change, agricultural productivity, and water and air quality are only a few of the environmental factors that are profoundly impacted by its expansion and development. The impact of urban heat islands (UHI) is exacerbated by changes in land topog- raphy, which are mostly caused by industry and urbanization. The effects of urban heat islands (UHI) are especially noticeable in dry climates or places where cities are expanding rapidly without adequate planning. However, consistent surface monitoring of the Earth is required for a complete understanding, which is required for UHI detection and reduction. Nevertheless, a great deal of effort, time, and materials are needed for this task. To address this issue, many computational and numerical methods have been developed to identify the emergence and propagation of UHI. Urban Canopy Model (UCM), Building Energy Model (BEM), and Urban Heat Island Intensity (UHII) are three prominent examples of such approaches. For this pur- pose, remote sensing photography has a lot of potential as a tool for investigating and forecast- ing UHI mechanisms. Utilizing Remote Sensing data acquired in Chennai, India, this study examines Land Surface Temperature (LST) and plant health with the purpose of evaluating UHI. Analyzing changes in land use and land cover (LULC) using classifiers such as Support Vector Machine (SVM), Random Forest (RF), and Maximum Likelihood Classification (MLC) evaluates plant health. Classifiers like this make it simpler to sort land into various buckets by showing how land cover has changed over time. The link between land cover change and UHI formation may be better understood with the use of classifiers, which organize pixels into clas- ses based on their similarity.