In order to accurately measure the abnormal situation of distribution network line loss and ensure the stable operation of the distribution network economy, a distribution network line loss anomaly detection method combining grey correlation analysis, improved K-means algorithm, and isolated forest algorithm is proposed based on the existing line loss anomaly detection methods, which have problems such as poor detection accuracy and low efficiency. Optimize the isolated forest algorithm through grey correlation analysis and improved K-means algorithm, complete feature attribute screening through grey correlation analysis, and complete data clustering processing through improved K-means algorithm, the accuracy and efficiency of anomaly detection in the isolated forest algorithm are improved. The results show that the proposed method has the best testing effect on multiple indicators compared to conventional methods, with a testing accuracy of 100% and an average detection time is 0.0402 seconds, providing certain support for achieving the carbon peaking and carbon neutrality goals.