Traditional Cloud Computing networks are intensely centralized in which the data is collected at the edges and transmitted back to the central network servers for computation. Due to the dramatic increase of IoT devices, such edges lack the computational power to handle the data collection and storage over the network because of the assumption of devices located closer to the edges. Edge Computing (EC) broadens the cloud computing characteristics of gathering, storing, processing, and analyzing a massive amount of data by locating services close to the edge of the network. Yet, the unique features of Edge Computing have introduced several challenging issues in the data handling process. The paper provides an overview of the data handling challenges faced in the Edge Computing network. It defines the fundamentals of Edge Computing - the basic architecture, how it's different from Cloud Computing, its applications, and discusses the threats encountered in Edge Computing. There are various challenges experienced in EC while storing, managing, and analyzing data over the network through different local Edge Nodes. This paper summarizes the solutions to the proposed problems in EC through different machine learning and deep learning algorithms. It also provides future research directions in edge computing. [ABSTRACT FROM AUTHOR]