In this paper, we find that an innovative approach to cloud-edge collaborative task scheduling optimization is presented, integrating Deep Q-Learning (DQN) with Genetic Algorithms (GA). By utilizing DQN for real-time decision-making and GA for global optimization, this method effectively handles challenges including latency, load balancing, and resource utilization. In addition, we designed a multi-agent task scheduling environment using OpenAI’s Gym framework to model cloud-edge systems with dynamic task migration, aiming to balance loads and reduce delays. Experiments reveal that the combined method outperforms traditional algorithms—including Greedy Algorithm, Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing—in enhancing latency, load balancing, and resource allocation. These results validate the model’s robustness in optimizing task distributions and adapting to changing workloads, which means providing an effective solution for complex cloud-edge computing challenges.