The proposed work explores the role of Large Language Models (LLMs) in transforming Urban Development (UD) by addressing city issues. The study focuses on approaches when processing structured and unstructured text data by the LLMs, providing actionable insights to UD planners. LLMs play a crucial role in shaping UD with enhanced decision-making in many ways, such as area zoning, transportation management, waste material handling, and environmental sustainability, by summarizing intricate reports and analyzing citizen feedback. These capabilities allow a holistic approach to UD, addressing population growth requirements with improved sustainability and efficiency. The manuscript delves into exploring the distinct benefits of integrating LLMs in the UD process. For instance, the impact of LLMs in diminishing traffic congestion, improving energy efficiency, and optimizing waste collection routes leads to a considerable reduction in operating costs and carbon footprints. Imparting LLMs in citizen engagement ensures additional focus on UD as these models are adept at multilingual processing, collection of feedback from diverse populations, revealing requirement patterns, and highlighting the concerned area becomes more effortless. This fosters a responsive, unbiased, transparent relationship between UD planners and citizens. To conclude, the proposed work underscores the significant role of LLMs in shaping the future of innovative and sustainable cities. Further, LLMs facilitate UD planning with improved data-driven decisions, effectively addressing the complexities of UD.