Anomaly detection is a critical task in data mining, aimed at identifying rare instances that deviate from the norm within a dataset. Anomaly detection has been greatly enhanced by machine learning models, which can understand intricate data patterns and correlations. This paper provides a comprehensive overview of machine learning models commonly used for anomaly detection in various domains. We discuss the principles underlying these models, their advantages and limitations, and their applications in real-world scenarios. Additionally, we highlight key considerations in model selection and evaluation for effective anomaly detection. By synthesizing existing research and discussing current trends, this paper aims to provide insights into the state of the art in machine learning-based anomaly detection.