One of the primary problems facing the Internet of Things(IoT) that demands immediate attention is the issue of anomaly and identifying attacks in IoT environments. The IoT resources might fail as a result of abnormalities and attacks in the environment of the IoTs, such as probing, Denial of Service(DOS), Root to Local(R2L) and User to Root(U2R) attacks. To ensure the dependable performance of IoT systems, anomaly identification in the realm of IoT is a crucial responsibility. Manually detecting anomalous occurrences has grown more challenging with the abundance of data collected by IoT devices. As a result, machine learning(ML) methods have been suggested for IoT systems' anomaly recognition. A publicly available dataset for anomaly detection was used to assess the model. For data preparation, selecting features, and choosing models, the approach employs a number of ML techniques, including Decision Tree(DT), Logistic Regression(LR), Support Vector Machine(SVM) and Random Forest(RF). Criteria like recall, accuracy, precision, and F1-score have been used to measure these models' performance. The outcomes show that the employed strategy is effective in detecting network anomalies and improving the security of IoT systems. The system discovered that RF had the highest accuracy, with a rate of 98.47%, compared to LR, DT, and SVM, which had the accuracy of 97.83%, 98.22%, and 92.80%, respectively.