Context-aware mobility management in edge computing has emerged as a critical research area, aiming to ensure seamless service delivery and optimal resource utilization in highly dynamic mobile environments. Research papers in this domain explore strategies that consider user mobility patterns, network conditions, device capabilities, and application requirements to intelligently manage task offloading, handovers, and resource allocation across edge, fog, and cloud layers. Studies highlight the use of machine learning, deep reinforcement learning, and predictive analytics to anticipate mobility events and proactively adjust service placement, workload distribution, and network routing. Recent works focus on latency-sensitive and real-time applications such as autonomous vehicles, smart healthcare, augmented/virtual reality, and industrial IoT, where uninterrupted connectivity and low-latency computation are critical. Additionally, research emphasizes context-aware mechanisms that integrate user behavior, location, environmental conditions, and device constraints to optimize both Quality of Service (QoS) and Quality of Experience (QoE). Security- and privacy-preserving techniques, including federated learning and blockchain integration, are also being investigated to protect sensitive mobility and contextual data. Overall, context-aware mobility management in edge computing enables intelligent, adaptive, and resilient service delivery in heterogeneous and resource-constrained environments.