The increased use of IoT devices has created new hurdles in the detection of anomalies. Anomaly detection is the process of discovering unexpected or abnormal behaviour in a system, and anomalies in IoT systems can be produced by a variety of sources, including hardware and software faults, cyber assaults, and environmental conditions. Machine learning -based approaches for anomaly detection in IoT systems have emerged as a viable option, harnessing the capabilities of machine learning algorithms to detect and categorise anomalies in real -time. However, there are drawbacks to these approaches, such as data quality difficulties, the necessity for real-time analysis, and the possibility of false positives and false negatives. Organizations must carefully analyse the trade-offs associated in their implementation and deployment to overcome these problems. Based on research a review of machine learning-based algorithms for anomaly detection in IoT systems. We explore the problems and potential associated with these approaches, as well as a synopsis of available datasets and models. In addition, the article describes a framework for designing and testing machine learning-based algorithms for anomaly detection in IoT systems. Overall, machine learning-based technologies have the potential to transform the way we detect and respond to abnormalities in IoT systems, but their successful implementation necessitates a cautious and deliberate approach.