This research explores the analysis and visual representation of Land Surface Temperature (LST) in an urban area of Tamil Nadu India to handle essential problems from both rapid urbanization and climate change. Surface temperature variations generate important changes in local climate patterns which result in urban heat island development. LST functions as a vital measurement tool to check urban transformation and test different strategies which attempt to reduce heat impacts. This research makes efficient use of United States Geological Survey satellite data as well as Google Earth Engine capabilities to process vast geospatial dataset information. The analysis depth becomes stronger through the application of machine learning and advanced data processing methods that run under Google Colab. The presented study displays comprehensive visual representations of LST spatial-temporal patterns while showing how land use and population patterns together with vegetation cover affect surface temperatures. The researchers advocate for an interactive website as a method to display analysis results so policymakers and urban planners together with the general public can engage more effectively. This paper uses advanced methods to study urban climate processes which builds understanding for climate-resistant planning solutions that protect the environment as cities grow in response to climate change.