The exponential growth of artificial intelligence (AI) and data science applications has necessitated robust, scalable, and accessible infrastructure. Cloud platforms have emerged as the de facto environment for developing, training, and deploying intelligent systems by offering managed services for data ingestion, model building, and API-driven inference. This article explores how leading cloud providers—Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)—facilitate end-to-end AI and data science workflows. It presents a methodological framework for selecting and utilizing cloud-native AI services, interprets performance data related to scalability and cost-efficiency, and concludes with strategic recommendations for practitioners. The findings indicate that cloud platforms reduce time-to-deployment by over 40% compared to on-premises solutions while democratizing access to high-performance computing.