Urban planning is an inherently complex and resource-intensive process characterized by lengthy timelines and extensive bureaucratic procedures. A team of urban planners, often comprising multiple professionals, must process vast datasets, engage in iterative planning cycles, and conduct rigorous assessments to develop actionable urban development strategies. This conventional approach is time-consuming and costly and frequently delays timely urbanization, particularly in rapidly developing regions. In response to these challenges, we propose a novel AI-driven pipeline designed to streamline the creation and evaluation of satellite imagery for urban planning applications. At its core, the pipeline integrates an inpaint diffusion model within a generative AI framework, enabling the automated generation of high-quality, contextually relevant satellite images, reducing time for urban planning from years to seconds. In order to make sure the generation is accurate and useful, after adversarial evaluation using a GANs methodology, achieving beyond 80% of similarity between data consisting of the traditional approach of urban development and the model’s generation, the potential usage and feasibility of our system for practical integration into the urban development process is displayed.