Scheduling is an NP-hard problem, and heuristic algorithms are unable to find approximate solutions within a feasible time frame. Efficient task scheduling in Cloud Computing (CC) remains a critical challenge due to the need to balance energy consumption and deadline adherence. Existing scheduling approaches often suffer from high energy consumption and inefficient resource utilization, failing to meet stringent deadline constraints, especially under dynamic workload variations. To address these limitations, this study proposes an Energy-Deadline Aware Task Scheduling using the Water Wave Optimization (EDATSWWO) algorithm. Inspired by the propagation and interaction of water waves, EDATSWWO optimally allocates tasks to available resources by dynamically balancing energy efficiency and deadline adherence. The algorithm evaluates tasks based on their energy requirements and deadlines, assigning them to virtual machines (VMs) in the multi-cloud environment to minimize overall energy consumption while ensuring timely execution. Google Cloud workloads were used as the benchmark dataset to simulate real-world scenarios and validate the algorithm's performance. Simulation results demonstrate that EDATSWWO significantly outperforms existing scheduling algorithms in terms of energy efficiency and deadline compliance. The algorithm achieved an average reduction of energy consumption by 21.4%, improved task deadline adherence by 18.6%, and optimized resource utilization under varying workloads. This study highlights the potential of EDATSWWO to enhance the sustainability and efficiency of multi-cloud systems. Its robust design and adaptability to dynamic workloads make it a viable solution for modern cloud computing environments, where energy consumption and task deadlines are critical factors.