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The rapid advancement of machine learning (ML) necessitates scalable, efficient, and cost-effective computing environments. Traditional ML workflows often face challenges related to long training times, high inference latency, infrastructure costs, and scalability limitations. This study explores the role of cloud-native architectures in accelerating ML workflows through data engineering innovations. By leveraging microservices, containerization, serverless computing, and automated data pipelines, cloud-native environments optimize ML operations while reducing computational overhead. The results indicate a 50% reduction in training time and inference latency, 50-55% cost savings, and a threefold increase in scalability compared to traditional ML implementations. Moreover, cloud-native solutions enhance fault tolerance by reducing system recovery time by 80%, ensuring greater reliability for real-time AI applications. Statistical analyses, including regression modeling, survival analysis, and PCA, confirm the efficiency gains of cloud-based ML workflows. The findings suggest that organizations adopting cloud-native ML architectures can achieve faster model deployment, reduced infrastructure costs, and enhanced system resilience. As cloud-native computing evolves, its integration with machine learning will play a pivotal role in shaping future AI-driven solutions