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Anomaly detection has been used for decades to identify and extract anomalous components from data. Many techniques have been used to detect anomalies. One of the increasingly significant techniques is Machine Learning (ML), which plays an important role in this area. In this research paper, we conduct a Systematic Literature Review (SLR) which analyzes ML models that detect anomalies in their application. Our review analyzes the models from four perspectives; the applications of anomaly detection, ML techniques, performance metrics for ML models, and the classification of anomaly detection. In our review, we have identified 290 research articles, written from 2000-2020, that discuss ML techniques for anomaly detection. After analyzing the selected research articles, we present 43 different applications of anomaly detection found in the selected research articles. Moreover, we identify 29 distinct ML …