This study examines the transformative role of artificial intelligence (AI) in advancing organizational environmental sustainability. It explores how AI-driven innovations—spanning predictive analytics, decision optimization, and smart execution systems—address critical challenges in climate governance, resource efficiency, and ecological preservation. The research highlights paradigm shifts in environmental management, transitioning from reactive models to AI-enabled cognitive augmentation, self-organizing systems, and negentropy-driven circular economies. Through case studies such as AI-powered wildfire prediction, dynamic energy grids, and robotic environmental remediation, the paper demonstrates AI's capacity to reduce emissions, enhance resource recovery, and mitigate ecological risks. However, it also critically analyzes technical, institutional, and social challenges, including algorithmic opacity, regulatory fragmentation, and equity concerns. The study proposes a roadmap for cross-domain integration, emphasizing quantum computing, neuromorphic architectures, and blockchain-enhanced governance frameworks. By advocating for balanced human-AI collaboration and systemic institutional innovations, the research charts a path toward intelligent ecological civilization through adaptive, equitable, and transparent AI solutions.