The environment, as a dynamic system influenced by anthropogenic factors, requires evidence-based decision-making to ensure sustainable resource management and mitigate adverse effects. This chapter explores the transformative role of artificial intelligence (AI) in environmental decision support systems (EDSS), focusing on its potential to address complex environmental challenges. EDSS, characterized by their interactivity and ability to process unstructured data, have evolved significantly since their inception in the late twentieth century. However, their adoption has been hindered by mismatches between system outputs and user expectations, as well as a lack of trust and standardized evaluation criteria. The integration of AI technologies, such as machine learning, predictive analytics, and computer vision, has revolutionized the functionality of EDSS, enabling real-time data analysis, improved forecasting, and enhanced decision-making processes. This chapter highlights the symbiotic relationship between AI advancements and urban development, demonstrating how AI-driven tools optimize resource allocation, improve strategic planning, and contribute to sustainable growth. Case studies illustrate the application of AI-based EDSS in areas such as climate risk prediction, disaster management, and environmental impact assessments for infrastructure projects. By examining the drivers and barriers to EDSS implementation, this chapter underscores the importance of interdisciplinary approaches and robust AI models in addressing global environmental challenges. The findings emphasize that AI-powered EDSS are essential for navigating the complexities of modern environmental management and achieving long-term sustainability goals.