This article explores the transformative potential of integrating artificial intelligence (AI) with environmental science to address pressing global challenges related to sustainability. The interdisciplinary synergy between AI technologies and environmental science is examined across key domains, including environmental monitoring, predictive modeling for climate change, conservation and biodiversity, and sustainable resource management. The article highlights the role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion. Emphasizing the significance of collaborative efforts, the abstract underscores the need for interdisciplinary insights to harness the full potential of AI in promoting environmental sustainability. The introduction sets the stage by discussing the emerging intersection of AI and environmental science, emphasizing the need for innovative approaches to enhance our understanding of the environment and develop effective solutions. The article aims to explore the synergies between AI and environmental science, delving into various case studies, methodologies, and applications where AI is being harnessed to advance environmental systems, optimize resource allocation, and develop sustainable solutions. The objectives of the article include investigating the current state of integration, exploring promising applications and case studies, and proposing strategies for future collaboration and innovation. The literature review highlights the potential of AI in contributing to sustainable solutions in environmental science, while the methodology section introduces the Sustainable AI Assessment Framework (SAAIF), which considers the social, ecological, economic, and organizational governance dimensions of sustainability. The article also presents an embedded perspective on sustainable AI, focusing on the complex relationships between AI systems and the socio-technical-ecological fabric of our world. The sustainability assessment of AI systems is structured into four crucial impact levels: the AI System Level, the Application Level, the Macro-Social Level, and the Ecological System Level. The article concludes by discussing the challenges and implications for AI development, research, and policy, emphasizing the need for regulations, industry standards, and further research to address the interconnectedness and entanglements of sustainability impacts in AI systems.This article explores the transformative potential of integrating artificial intelligence (AI) with environmental science to address pressing global challenges related to sustainability. The interdisciplinary synergy between AI technologies and environmental science is examined across key domains, including environmental monitoring, predictive modeling for climate change, conservation and biodiversity, and sustainable resource management. The article highlights the role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion. Emphasizing the significance of collaborative efforts, the abstract underscores the need for interdisciplinary insights to harness the full potential of AI in promoting environmental sustainability. The introduction sets the stage by discussing the emerging intersection of AI and environmental science, emphasizing the need for innovative approaches to enhance our understanding of the environment and develop effective solutions. The article aims to explore the synergies between AI and environmental science, delving into various case studies, methodologies, and applications where AI is being harnessed to advance environmental systems, optimize resource allocation, and develop sustainable solutions. The objectives of the article include investigating the current state of integration, exploring promising applications and case studies, and proposing strategies for future collaboration and innovation. The literature review highlights the potential of AI in contributing to sustainable solutions in environmental science, while the methodology section introduces the Sustainable AI Assessment Framework (SAAIF), which considers the social, ecological, economic, and organizational governance dimensions of sustainability. The article also presents an embedded perspective on sustainable AI, focusing on the complex relationships between AI systems and the socio-technical-ecological fabric of our world. The sustainability assessment of AI systems is structured into four crucial impact levels: the AI System Level, the Application Level, the Macro-Social Level, and the Ecological System Level. The article concludes by discussing the challenges and implications for AI development, research, and policy, emphasizing the need for regulations, industry standards, and further research to address the interconnectedness and entanglements of sustainability impacts in AI systems.