AI-Guided Load Balancing in B-Trees, Hash Tables, and Distributed File Systems

Efficient load balancing is critical to maintaining high performance, scalability, and fault tolerance in modern data-intensive applications. Traditional data structures such as B-Trees, hash tables, and distributed file systems provide deterministic and well-understood organization methods, yet they often fail to adapt dynamically to rapidly changing workloads and access patterns. This research introduces an AI-guided load balancing framework that integrates machine learning models into the operational logic of these data structures, enabling real-time adaptive optimization. The proposed methodology leverages predictive analytic to monitor workload trends, identify potential hot-spots, and proactively redistribute data before performance degradation occurs. In B-Trees, AI enhances node-splitting and merging decisions; in hash tables, it refines bucket allocation strategies; and in distributed file systems, it improves data replication and placement policies. Experimental evaluation across diverse workload scenarios demonstrates that the AI-guided approach outperforms static and rule-based balancing techniques, achieving up to 35% reduction in query latency, 28% improvement in throughput, and enhanced resilience under skewed access patterns. These results confirm that embedding intelligence into foundational data structures not only bridges the gap between theoretical algorithms and adaptive computing but also sets a pathway for hybrid systems that combine classical rigor with learning-driven adaptability. The findings have implications for large-scale database systems, cloud storage platforms, and distributed computing environments highlighting the transformative potential of AI enhance core computing paradigms.