Machine learning (ML) is transforming industries by automating tasks and generating insights from large datasets. Cloud computing platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) provide essential resources that enhance the development and deployment of ML models. This paper provides a comprehensive comparative analysis of these cloud platforms, highlighting their ML services, scalability, cost structures, and integration capabilities. Through case studies and performance benchmarks, we explore how these platforms facilitate different stages of the ML pipeline, addressing the strengths and limitations of each. The study serves as a guide for practitioners and researchers looking to leverage cloud platforms for ML applications [6], [7].