Edge computing has emerged as a transformative technology to address the limitations of traditional cloud-centric approaches in the Internet of Things (IoT) ecosystem. By bringing computational resources closer to data sources, edge computing minimizes latency, reduces bandwidth usage, and enables real-time data processing, crucial for IoT applications such as healthcare, smart cities, and industrial automation. This study explores the synergy between edge computing and IoT, focusing on architectural paradigms, challenges, and opportunities. Through a systematic review of 50 recent research sources, the study identifies key architectural models, including three-tier, fog computing, and hybrid systems. It highlights critical challenges, such as resource limitations, security concerns, and interoperability issues, while also uncovering opportunities for innovation, particularly in real-time analytics and AI- driven edge intelligence. The findings emphasize the need for collaborative efforts in addressing these challenges and advancing the edge-IoT integration. The study concludes with future research directions, including lightweight security protocols, improved scalability solutions, and universal interoperability standards.