Hybrid cloud environments often face challenges in balancing workloads, managing resources, and ensuring resilience, which can lead to performance bottlenecks and higher costs. To address this, the Hyper Responsive Artificial Intelligent based Hybrid Cloud Load Balancer (HRAIHCLB) is proposed as an AI-driven system that integrates three key modules: the AI Driven Load Prediction Distributor (ADLPD) for forecasting workloads and distributing traffic dynamically, the Cloud Orchestration and Resource Optimizer Module (COROM) for seamless public–private cloud integration, energy-efficient scaling, and cost optimization, and the Resilience and Monitoring Module (RMM) for security enforcement, compliance, and adaptive learning. Evaluated on metrics such as load balancing efficiency, resource utilization, response time, migration cost, and throughput, HRAIHCLB demonstrated significant improvements in adaptability, cost reduction, and resilience, making it a promising solution for enterprise hybrid cloud management with potential future applications in multi-cloud and edge computing ecosystems.
