In recent decades, the Internet of Things (IoT) has grown rapidly, attracting the attention of scientists and businesspeople. In extreme conditions, autonomously scattered sensor nodes pose a high risk of failure and intrusion into the IoT, skewing sensor values. Abnormal data, anomalies, or outliers are sensor values that depart from norms. When abnormalities are factored into data analytics, the ultimate judgment is affected. Using data-driven algorithms for IoT outlier detection is a cutting-edge tactic in Machine Learning (ML). However, evaluating the effectiveness of implemented ML techniques for outlier detection in IoT, which have the minimal processing power and power sources to ensure data quality, raises several difficulties that have just recently begun to be addressed in the academic literature. This paper analyses the cutting-edge architecture, type, degree, technique, and detection mode of AI and statistical outlier detection strategies in IoTs. Also, each of the ways to find outliers is talked about in detail, along with ways to make them better.