According to the World Urbanization Prospects report by the United Nations, the global urban population has grown significantly in recent decades, with 55% of the population living in urban areas in 2018. This figure is expected to rise to 68% by 2050. In the context of Asia, rapid urban expansion and widespread infrastructure development have contributed to the growing challenge of the urban heat island (UHI) effect, along with its associated environmental and social issues. Despite these concerns, many cities are not giving adequate attention to implementing effective heat mitigation strategies, which could exacerbate the long-term impacts of UHI. This phenomenon of rapid urbanization is leading to climate change, forming a great threat to humans and the surrounding ecosystem. Keeping the aforesaid knowledge and understanding of the functional linkages between urban morphology and UHI intensity, Chennai, a Coastal City of Southern India has been chosen for further detailed investigation. This research study delves into exploring intricate relationships through selected regions by using ENVI-Met simulation techniques, in diverse urban fabrics with different land uses under various Local Climate Zones and with an evolved UHI prediction model for the projected year 2043 A.D., by employing Machine-Learning Algorithms—Artificial Neural Network, Cellular Automata modeling. Based on the results an integrated planning framework was developed to recommend planning guidelines and mitigation strategies and to suggest an Area, Zone, Point Approach (AZP Approach) at the Neighborhood level and City level to achieve sustainable urban development of the study region.