Artificial Intelligence (AI) is advancing at an unprecedented pace, propelled by a convergence of machine learning, reasoning, search, and optimization methods, many of which have relevance to freshwater science. From computer vision and real-time language translation to world-champion Go/Chess engines, self-driving cars, and powerful models like ChatGPT, AI’s expanding capabilities open exciting frontiers in scientific discovery and real-world problem-solving. I will introduce Computational Sustainability, a rapidly growing interdisciplinary field devoted to developing computational models and techniques that balance environmental, economic, and societal needs for a sustainable future. I will showcase illustrative projects, including biodiversity and conservation, strategic hydropower dam planning in the Amazon basin, and materials discovery for renewable energy. These endeavors highlight cross-cutting computational themes—constraint and multi-agent reasoning, optimization, machine learning, citizen science, and crowdsourcing—that offer transformative solutions. AI-driven approaches can advance freshwater science—whether through safeguarding aquatic biodiversity, optimizing water resources, or mitigating environmental impacts—and, more generally, can play a pivotal role in forging a sustainable path forward.