In "Outlier Detection for IoT Frameworks Using Isolation Forest," it highlights the need of identifying anomalous data in Internet of Things (IOT) environments, where enormous volumes of sensor data are transmitted over wireless networks. In IoT frameworks, identifying anomalies is crucial for network security, problem solving, and efficient data management. Issues in this field include high data volume and velocity, resource constraints on IoT devices, fluctuating network conditions, and the difficulty of distinguishing between real outliers and assaults or sensor faults. These problems are addressed by a variety of machine learning algorithms, such as K-Means Clustering and DBSCAN for classifying similar patterns and identifying outliers, Isolation Forest and One-Class SVM for unsupervised anomaly detection, and Neural Networks and Auto encoders for deep learning-based anomaly detection in complex, high- dimensional IoT data. These techniques look at network traffic, sensor readings, and device behaviors to improve system security and efficiency. This approach guarantees intelligent, safe, and dependable IoT activities in wireless settings. Among its uses are traffic optimization in smart transportation networks, network anomaly detection in healthcare monitoring systems, fault prediction in industrial IoT, and intrusion detection in smart cities.