The paper "Integrating Artificial Intelligence and Environmental Science for Sustainable Urban Planning" by Muhammad Rehan Anwar and Lintang Dwi Sakti explores the integration of Artificial Intelligence (AI) and Environmental Science to enhance urban planning processes. The study aims to assess the impact and utility of AI techniques within the framework of Geographic Information Systems (GIS) for sustainable urban planning. Specifically, it investigates how AI-enhanced GIS tools can improve urban development strategies and sustainability assessments. The research employs spatial analysis with GIS to analyze data on land use, population density, and environmental indicators across several metropolitan areas. Machine learning algorithms are used to predict and simulate urban growth patterns, enabling the assessment of various urban planning scenarios. The findings reveal that AI-enhanced GIS tools significantly improve the precision of development forecasts and sustainability assessments, facilitating more informed decision-making in urban planning. The integration of AI with environmental science not only enhances the efficiency of urban planning processes but also contributes to the resilience and sustainability of urban environments. The study provides urban planners and policymakers with advanced tools to forecast and mitigate the environmental impacts of urbanization, setting a benchmark for future studies in sustainable urban planning. The research demonstrates the practical application of AI in enhancing the capabilities of GIS for complex spatial analyses, contributing significantly to the field of urban planning.The paper "Integrating Artificial Intelligence and Environmental Science for Sustainable Urban Planning" by Muhammad Rehan Anwar and Lintang Dwi Sakti explores the integration of Artificial Intelligence (AI) and Environmental Science to enhance urban planning processes. The study aims to assess the impact and utility of AI techniques within the framework of Geographic Information Systems (GIS) for sustainable urban planning. Specifically, it investigates how AI-enhanced GIS tools can improve urban development strategies and sustainability assessments. The research employs spatial analysis with GIS to analyze data on land use, population density, and environmental indicators across several metropolitan areas. Machine learning algorithms are used to predict and simulate urban growth patterns, enabling the assessment of various urban planning scenarios. The findings reveal that AI-enhanced GIS tools significantly improve the precision of development forecasts and sustainability assessments, facilitating more informed decision-making in urban planning. The integration of AI with environmental science not only enhances the efficiency of urban planning processes but also contributes to the resilience and sustainability of urban environments. The study provides urban planners and policymakers with advanced tools to forecast and mitigate the environmental impacts of urbanization, setting a benchmark for future studies in sustainable urban planning. The research demonstrates the practical application of AI in enhancing the capabilities of GIS for complex spatial analyses, contributing significantly to the field of urban planning.