As large enterprises increasingly adopt Artificial Intelligence (AI) to drive innovation and maintain competitive advantage, the demand for scalable and efficient cloud-based data architectures has surged. Optimizing these architectures is critical to support the vast computational and storage requirements of AI applications while ensuring performance, reliability, and cost-effectiveness. This research explores the key strategies and technologies for optimizing cloud-based data architectures to facilitate scalable AI deployments in large enterprises. We examine architectural frameworks, data management practices, resource allocation techniques, and integration of advanced cloud services. Through case studies and performance evaluations, we demonstrate how optimized cloud architectures enhance AI application scalability, reduce latency, and lower operational costs. Our findings provide a comprehensive guide for enterprises seeking to leverage cloud infrastructure to scale their AI initiatives effectively.