This article explores the integration of artificial intelligence (AI) with environmental science to address global sustainability challenges. It emphasizes the importance of interdisciplinary collaboration between AI and environmental science to develop innovative solutions for climate change, biodiversity loss, and resource depletion. The article discusses key areas where AI can contribute, including environmental monitoring, predictive modeling, conservation, and sustainable resource management. It highlights the role of AI in real-time data analysis, pattern recognition, and optimization, offering new insights into ecological dynamics and environmental risks. The article outlines three main objectives: (1) to investigate the current state of AI integration in environmental science, (2) to explore promising applications and case studies, and (3) to propose strategies for future collaboration and innovation. It also presents a literature review that discusses the potential of AI in environmental monitoring and management, as well as the need for interdisciplinary collaboration to fully harness AI's potential for sustainability. The methodology includes case study selection, data collection and analysis, evaluation of AI-driven solutions, and synthesis of findings. The article introduces the Sustainable AI Assessment Framework (SAAIF), which considers social, ecological, economic, and organizational governance dimensions of sustainability. This framework provides a holistic approach to evaluating the sustainability impacts of AI systems. The article also discusses the challenges and implications of AI development, research, and policy, emphasizing the need for regulations, industry standards, and further research to address the complex interdependencies of AI sustainability. It concludes by highlighting the importance of a comprehensive and holistic approach to ensure the sustainability of AI systems, with a focus on practical implementation and ongoing research.This article explores the integration of artificial intelligence (AI) with environmental science to address global sustainability challenges. It emphasizes the importance of interdisciplinary collaboration between AI and environmental science to develop innovative solutions for climate change, biodiversity loss, and resource depletion. The article discusses key areas where AI can contribute, including environmental monitoring, predictive modeling, conservation, and sustainable resource management. It highlights the role of AI in real-time data analysis, pattern recognition, and optimization, offering new insights into ecological dynamics and environmental risks. The article outlines three main objectives: (1) to investigate the current state of AI integration in environmental science, (2) to explore promising applications and case studies, and (3) to propose strategies for future collaboration and innovation. It also presents a literature review that discusses the potential of AI in environmental monitoring and management, as well as the need for interdisciplinary collaboration to fully harness AI's potential for sustainability. The methodology includes case study selection, data collection and analysis, evaluation of AI-driven solutions, and synthesis of findings. The article introduces the Sustainable AI Assessment Framework (SAAIF), which considers social, ecological, economic, and organizational governance dimensions of sustainability. This framework provides a holistic approach to evaluating the sustainability impacts of AI systems. The article also discusses the challenges and implications of AI development, research, and policy, emphasizing the need for regulations, industry standards, and further research to address the complex interdependencies of AI sustainability. It concludes by highlighting the importance of a comprehensive and holistic approach to ensure the sustainability of AI systems, with a focus on practical implementation and ongoing research.