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QECO is designed to balance and prioritize QoE factors based on individual mobile device requirements while considering the dynamic workloads at the edge nodes. The QECO algorithm captures the dynamics of the MEC environment by integrating the Dueling Double Deep Q-Network (D3QN) model with Long Short-Term Memory (LSTM) networks. This algorithm address the QoE maximization problem by efficiently utilizing resources from both MDs and ENs. D3QN: By integrating both double Q-learning and dueling network architectures, D3QN overcomes overestimation bias in action-value predictions and accurately identifies the relative importance of states and actions. This improves the model’s ability to make accurate predictions, providing a foundation for enhanced offloading strategies. LSTM: Incorporating LSTM networks allows the model to continuously estimate dynamic work- loads at edge servers. This is crucial for dealing with limited global information and adapting to the uncertain MEC environment with multiple MDs and ENs. By predicting the future workload of edge servers, MDs can effectively adjust their offloading strategies to achieve higher QoE. Simulation results reveal that compared to the state-of-the-art existing works, QECO increases the number of completed tasks by up to 14.4%, while simultaneously reducing task delay and energy consumption by 9.2% and 6.3%, respectively. Together, these improvements result in a significant average QoE enhancement of 37.1%. This substantial improvement is achieved by accurately accounting for user dynamics and edge server workloads when making intelligent offloading decisions. This highlights QECO’s effectiveness in enhancing users’ experience in MEC systems.