The exponential growth of data generation in modern computing environments necessitates efficient optimization strategies for distributed computing systems handling real-time data processing. This paper presents a novel machine learning-based optimization framework that dynamically adjusts resource allocation, load balancing, and task scheduling in distributed computing environments. Our proposed methodology integrates reinforcement learning algorithms with predictive analytics to achieve optimal system performance while maintaining low latency requirements. Experimental results demonstrate a 34% improvement in processing efficiency and 28% reduction in response time compared to traditional optimization approaches. The framework successfully handles varying workloads and adapts to changing system conditions in real-time scenarios.