<![CDATA[The rapid growth of data-intensive applications and increasingly complex machine learning (ML) models has created an urgent need for computational architectures capable of supporting large-scale intelligent systems. This research presents a foundational study on integrating machine learning with distributed computing to achieve scalable, high-performance AI workflows. The study develops a conceptual integration model comprising four core layers data, compute, communication, and model designed to address scalability, fault tolerance, and resource optimization. Using experimental benchmarking and architectural analysis, the research evaluates multiple distributed frameworks, data partitioning strategies, and ML models to measure improvements in training speed, throughput, latency, and resource utilization across cluster-based and cloud environments. Results demonstrate significant performance gains compared to single-node execution, particularly for deep learning workloads, while also identifying critical bottlenecks such as communication overhead, synchronization delays, heterogeneous hardware constraints, and data imbalance. The findings highlight key trade-offs between accuracy and computational speed, as well as cost and system performance, underscoring the importance of strategic design decisions in large-scale ML deployments. This study contributes theoretical and practical insights into distributed ML integration and offers a framework that can guide the development of next-generation intelligent systems capable of operating across massively distributed environments.]]>
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