The Internet of Things (IoT) encompasses a vast array of smart devices that can collect, store, process, and transmit data. While IoT adoption has fueled significant innovation across industries, homes, environments, and businesses, its inherent vulnerabilities have raised concerns about its widespread deployment. Unlike traditional IT systems, securing the IoT is particularly challenging due to resource limitations, device heterogeneity, and the distributed nature of the network. Implementing host-based protection mechanisms, including antivirus and anti-malware software, is not feasible due to these issues. A monitoring strategy like anomaly detection, both at the device and network levels, becomes crucial beyond the organizational perimeter in light of these difficulties and the particulars of IoT applications. As such, anomaly detection systems are well-positioned to safeguard IoT devices more effectively than conventional security methods. In this paper, the authors highlight a comprehensive review of existing efforts to develop machine learning-based anomaly detection solutions for IoT security and explore how blockchain-integrated anomaly detection systems can collaboratively train machine learning models to enhance anomaly detection capabilities.