In recent times, the utilization of Artificial Intelligence has surged significantly, reaching various domains. Its transformative potential spans across diverse sectors, including education and urban planning. In education, AI offers personalized learning experiences by providing instant feedback and adapting instructional content to individual student needs. This not only benefits students but also supports teachers in engaging learners more accurately and effectively. AI can also be used to augment and better conceptualize more complicated ideas for both students and faculty, as stated in the following quote: “Throughout every stage of their work, engineers generate and utilize a vast quantity of documents. Hence, engineering students and faculty can use large-scale language models like ChatGPT and Bing Chat to improve their productivity, creativity, and quality.” (Goel 2023). In urban planning, AI aids in informed decision-making by analyzing extensive data on traffic flow, energy consumption, and public transport. It optimizes city services and infrastructure, enhancing urban life. AI also extends to predictive modeling and automation, optimizing city services like waste management and traffic control for increased efficiency and sustainability. For instance, AI-driven waste management optimizes collection routes, reducing emissions, while smart traffic control improves air quality and reduces congestion. AI has also been used to aid in blight detection in urban areas, urban blight standing for cities or towns that are in a state of disrepair or decay. Garbage trucks are used to survey and capture images of urban areas, the trucks park in a lot and upload the data through Wi-Fi, AI then uses those images to generate a blight score which represents that city's degree of decline. This process, if implemented, could aid in the maintenance and upkeep of various cities and towns across the country, preventing them from falling into severe disrepair. However, the AI must first be trained with images from various sources, which has its own set of problems, “The substantial amount of training data that AI models require can pose a challenge to organizations that do not have a robust photo inventory.” (AI IN CURRENT PRACTICE 2023).