Examining Climate Change Resilient Urban Areas with Artificial Intelligence (AI)-Supported Landscape Design

Climate change intensifies multidimensional environmental threats in urban areas, including flooding, drought, heatwaves, and biodiversity loss, making urban resilience a critical challenge. Developing climate-resilient cities therefore requires data-driven and integrative approaches in landscape design beyond conventional engineering solutions. In this context, artificial intelligence (AI), in combination with nature-based solutions (NBS), introduces new opportunities for the planning, implementation, and management of urban landscapes. The aim of this study is to investigate how AI-based decision support systems transform landscape design practices for climate change adaptation and ecological resilience. Using a descriptive content analysis method, selected national and international case studies focusing on plant selection, water management, green infrastructure optimization, and urban ecosystem monitoring were examined. The findings show that AI-supported plant selection models improve drought and heat tolerance while reducing irrigation demand; AI-based optimization of green infrastructure networks enhances stormwater management and significantly reduces urban flood risk; and sensor-based monitoring systems integrated with machine learning enable continuous assessment of ecosystem health and early detection of environmental stress. The study’s original contribution lies in demonstrating the integrated use of digital twins, machine learning algorithms, and environmental sensors at the landscape scale, providing a structured and transferable framework for climate-responsive design decisions. Overall, the results confirm that AI-supported urban landscape approaches not only increase resilience to climate change but also support the development of more sustainable, healthy, and livable urban environment.