Mobile Edge Computing (MEC) mitigates cloud computing systems’ latency and limited responsiveness by offloading computationally intensive tasks from user devices to nearby Edge Servers (ESs). However, achieving efficient offloading under dynamic mobility, fluctuating link quality, and constrained resources remains a significant challenge. To address this, we propose MSQ, a lightweight and adaptive three-dimensional decision offloading model that jointly incorporates Mobility, Sociality, and QoS awareness. MSQ employs Kalman filtering for mobility prediction, Rényi entropy to quantify social affinity among mobile users, and Affinity Propagation (AP) clustering to reduce redundant ES candidates while balancing computational load. Comprehensive experiments across small and medium-scale MEC networks demonstrate that MSQ reduces average task delay by up to 78%, energy consumption by 66%, and load imbalance by 64% compared with a random offloading strategy while having decision latency below one millisecond. Moreover, MSQ lowers the 95th-99th percentile tail delays by 35-45%, ensuring smoother and more reliable user experience in real- time applications. These results confirm that MSQ offers a scalable, low-latency, and energy-efficient offloading decision suitable for dynamic and intelligent edge systems.