Edge computing architectures and frameworks have become a central research theme in recent years, addressing the limitations of traditional cloud computing by bringing computation, storage, and intelligence closer to end devices. Research papers in this area focus on designing scalable, low-latency, and energy-efficient edge architectures that support emerging applications such as autonomous vehicles, smart healthcare, industrial IoT, and augmented/virtual reality. Studies highlight layered and hierarchical architectures that integrate edge, fog, and cloud computing, enabling seamless task offloading, resource orchestration, and service provisioning. Recent works explore software-defined networking (SDN), network function virtualization (NFV), and microservices-based frameworks to enhance flexibility and manageability. AI-driven and federated learning-enabled frameworks are also gaining momentum for adaptive decision-making, context awareness, and privacy preservation in distributed edge systems. Furthermore, research emphasizes challenges such as heterogeneity of devices, dynamic mobility, interoperability, and security in multi-tenant environments. Blockchain-based and zero trust architectures are being increasingly studied to build trust, ensure secure communication, and protect sensitive data. Overall, edge computing architectures and frameworks are evolving toward intelligent, resilient, and application-aware designs that enable real-time services in diverse cyber-physical ecosystems.