A significant number of methods have been devised for anomaly detection. Traditional methods like LOF, IForest, CBLOF, etc. have a strong performance and simple methodologies, making them very popular. However, the recently proposed methods of LUNAR and ECOD trump them with their better performance, where ECOD is a simpler, faster, and more interpretable method with a profound ability to detect global anomalies, and on the other hand, LUNAR introduces the trainability of parameters as a tremendous advantage and unifies the local outlier detection methods. These methods allow for the introduction of a new ensemble method that utilizes LUNAR and ECOD as base models, combined with Logistic regression as a meta model. This study introduces a technique that surpasses the original foundational techniques and other widely used outlier detection methods. The results showed that the proposed method had an 8.94% increase in accuracy and a 10.41% improvement in AUC score compared to the base models of ECOD and LUNAR. It consistently produces reliable and improved results compared to other algorithms.