The scheduling of tasks, usage of resources, and compliance with Service Level Agreement in dynamic and heterogeneous cloud systems are a challenge to cloud service providers. Traditional scheduling techniques are unlikely to manage the dynamism of workloads, resulting in the decline in performance, energy wastefulness and breach of Service Level Agreement. In this paper, the Broker-Driven Hybrid Wild Goose-Owl Optimization - Deep Reinforcement Learning Framework is suggested to integrate the Service Level Agreement-aware filtering of brokers with a two-level optimization pipeline. The broker filters the incoming tasks against SLA constraints. A Deep Reinforcement Learning agent makes the first task assignment depending on the system condition and projected SLA risk. The assignments are further optimized using a Hybrid Wild Goose-Owl Optimization algorithm to minimize the makespan, energy use, imbalance between the CPU processors, migration cost, and SLA violation rate. It has been experimentally demonstrated that the hybrid structure achieves a 32, 18, 58, and 74% reduction in the makespan, energy usage, CPU imbalance, and SLA violations, respectively, relative to baseline heuristics, and 41% reduction in SLA violations relative to DRL-only scheduling. These results prove that a combination of SLA intelligence broker and hybrid evolutionary and learning-based optimization can facilitate the management of cloud resources in a robust and scalable way.