Cloud computing is a prevalent technology in the IT market today, providing enterprises with the necessary infrastructure to achieve high performance levels for running their applications using the pay-as-you-go model. The lowest layer in Cloud is Infrastructure-as-a-Service (IaaS), which provides the resource pool. A primary challenge to cloud providers in IaaS is to effectively manage resources to reduce power consumption, which is a critical issue of paramount importance today, due to its substantial environmental impact stemming from the widespread use of cloud computing, while simultaneously maintaining Quality of Service. In this paper, we review the latest solutions that utilize Machine Learning (ML) algorithms for resource management. We focus on the processes of auto-scaling, Virtual Machine (VM) consolidation, and VM placement, which directly influence power consumption. We identify the benefits and innovations these solutions bring to the optimization of resource management, but also their drawbacks.