Learning-based Outlier Detection (LBOD) is an intelligent framework designed to identify abnormal or rare patterns in datasets using machine learning techniques. The model learns the underlying structure of normal data and detects deviations that indicate potential anomalies. By leveraging data-driven learning mechanisms, LBOD improves detection accuracy compared to traditional statistical methods. It is particularly useful in applications such as fraud detection, network intrusion detection, and financial risk analysis. Overall, LBOD provides an adaptive and scalable approach for identifying outliers in complex and high-dimensional data environments.