The growing use of cloud services in the contemporary society has put unprecedented pressure on scalability, responsiveness, and fault resilience, especially in multi -cloud environments that combine heterogeneous resources across providers. Such systems ha ve been difficult to balance their loads effectively because of workload variability, varying Service - Level Agreements (SLAs), and the necessity to allocate tasks efficiently with energy constraints of Ultra -Reliable Low -Latency Communication (URLLC). Trad itional approaches, such as heuristic schedulers, metaheuristic optimizers, and Reinforcement Learning (RL) -based solutions, have provided partial solutions but still have limitations in the form of slower convergence, high task latency, high migration ove rhead, and poor fault -tolerant behavior. In order to address these problems, a new framework is presented. The model incorporates four significant innovations. To facilitate privacy -preserving workload forecasting, Resource - Aware Federated Learning to Load Balancing (ResFedLB) is first used. Second, CNN -LSTM -Attention with Forecasting Transformer Network (CLAFT -Net ) is the specialized local training module that is used to learn temporal -spatia l workload dependencies. Third, Reinforce ment Learning with Coordination and Opposition Strategy in Coati (RL -COSCoati) is an adaptive and low -latency task scheduling. Lastly, CloudGuard is a fuzzy inference -based module, which guarantees proactive fault detection and resilient VM management. The framework is proven to be effective in experimental assessments with a forecasting accuracy of 99.38 and an F1 -score of 98.32, and task allocation performance of 97% Central Processing Unit ( CPU ) utilization and 98.20% task success rate. These findings pr ove the hypothesis that the suggested solution provides a scalable, energy - efficient, and fault -tolerant solution to managing dynamic workloads in multi -cloud ecosystems.

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