With the rapid growth of data volumes and cloud-based data warehouses, optimizing query performance while ensuring sustainable development has become crucial. This paper presents AI-powered automation frameworks designed to enhance query optimization within Snowflake-based data architectures. By leveraging machine learning models, automated workload management, and resource allocation techniques, the framework improves query execution efficiency, reduces computational costs, and minimizes energy consumption. The sustainable development focus emphasizes environmentally responsible data management practices without compromising performance. Experimental results demonstrate significant improvements in query latency, cost efficiency, and reduced carbon footprint, highlighting the framework's potential to support scalable and sustainable data operations in modern enterprises.