Abstract

Urban green spaces are vital for mitigating climate change, enhancing biodiversity, and improving citizen well-being. However, traditional methods of designing and managing these spaces often lack the precision and scalability needed to address modern urban challenges. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban green spaces in smart cities. By integrating satellite imagery, soil sensors, and machine learning models, cities can dynamically monitor plant health, predict ecological impacts, and design green zones that maximize biodiversity and sustainability. Experimental results demonstrate improvements in air quality, heat island reduction, and community engagement, offering a blueprint for AI-driven ecological urban planning.