The adoption of Internet of Things (IoT) sensing devices is growing rapidly due to their ability to provide real-time services. However, it is constrained by limited data storage and processing power. It offloads its massive data stream to edge devices and the cloud for adequate storage and processing. This further leads to the challenges of data outliers, data redundancies, and cloud resource load balancing that would affect the execution and outcome of data streams. This paper presents a review of existing analytics algorithms deployed on IoT-enabled edge cloud infrastructure that resolved the challenges of data outliers, data redundancies, and cloud resource load balancing. The review highlights the problems solved, the results, the weaknesses of the existing algorithms, and the physical and virtual cloud storage servers for resource load balancing. In addition, it discusses the adoption of network protocols that govern the interaction between the three-layer architecture of IoT sensing devices enabled edge cloud and its prevailing challenges. A total of 72 algorithms covering the categories of classification, regression, clustering, deep learning, and optimization have been reviewed. The classification approach has been widely adopted to solve the problem of redundant data, while clustering and optimization approaches are more used for outlier detection and cloud resource allocation.