AI-Enabled Learning for Climate Action brings together scholars, practitioners, policymakers, and innovators to examine how digital learning systems, data platforms, and emerging technologies-including artificial intelligence, digital twins, and immersive tools-can accelerate climate literacy, support green skills and jobs, strengthen institutional decision-making, and enable collective action. Rather than treating digital learning as a delivery mechanism alone, the contributors demonstrate how learning systems function as critical climate infrastructure by building the human and institutional capacities needed for adaptation, mitigation, and long-term resilience. The chapters span global, regional, and local contexts, with examples from low-, middle-, and high-income countries. They explore how AI-enabled and digital learning approaches can enhance climate awareness and behavior change; equip youth and professionals with future-ready and green skills; support evidence-based policymaking; and enable experiential, problem-based learning grounded in real-world data. Contributors also examine how technology-enabled learning can advance climate justice, strengthen governance, and promote transparency, public participation, and accountability. Blending conceptual frameworks with applied case studies, this volume offers both the what and the how of designing inclusive, scalable, and action-oriented climate learning ecosystems. It is intended for educators, policymakers, development practitioners, and institutions seeking practical guidance on how AI-enabled learning can help bridge the gap between climate knowledge, skills for the future of work, and meaningful action.