An Urban Heat Island is a metropolitan area with higher air and surface temperatures than surrounding areas. The Urban Heat Island Effect (UHIE) is a relative measure of the heat in urban heat islands. This research study investigates how developed land cover and weather trends can be used to forecast the UHIE with two distinct modeling frameworks. Projections of future conditions can prepare scientists and communities to take greener initiatives and adapt their lifestyle to preserve the Earth. The study focuses on the Greater Austin Region (TX, USA) for initial feasibility, but aims to extend these methods to a national or global scale. The first technique uses machine learning (Keras sequential model) to identify correlations between factors closely linked to the UHIE. The tested factors were air and surface temperature, relative humidity, soil moisture, and population growth. Evident correlations were found and used to begin training a predictive model (artificial neural network). The second technique uses developed softwares in QGIS Modules for Land Use Change Evaluation (MOLUSCE), high resolution satellite imagery provided by Multi-Resolution Land Characteristics land cover/land use data, and distance from roadways and inland water bodies data in order to accurately predict the possible changes in 2022 to the Greater Austin Region. Major limitations throughout the research process include regional & temporal data inconsistencies, the narrow scope of factors and geographic region, and the time constraint of the NASA SEES internship. Given ample time and data, these analyses can be used in green efforts to moderate and reduce the causes of UHIE. They can also aid in further investigating water contamination, energy consumption, and human health, and make larger scale environmental simulations possible.