The rapid growth of Internet of Things (IoT) devices has resulted in enormous amounts of real-time data, requiring effective strategies for processing, storing, and managing information. Traditional cloud-centric models often struggle with high latency and bandwidth limitations, making them unsuitable for time sensitive and mobility driven applications. Edge computing overcomes the challenges of traditional cloud models by relocating computation and storage functions closer to where data is generated. This significantly reduces latency and enables more responsive, context-aware processing. This paper delivers an in-depth analysis of data management strategies in edge computing, highlighting their essential role in building scalable and intelligent IoT-based systems. The paper discusses about different data management techniques, including data caching, data storage, data aggregation and data integrity. It also highlights the inherent challenges such as resource constraints, security, consistency, and mobility, and identifies emerging research directions. By synthesizing current methodologies and outlining future opportunities, this study aims to guide the development of robust and efficient data management frameworks for next-generation edge computing systems.