There is a limitation of manual setting of an abnormal data judgment threshold and there will be inefficient filling of missing data in the traditional process of data cleaning in a distribution network. This paper proposes an integrated dynamic cleaning method for distribution network abnormal and missing data based on machine learning. First, based on a local outlier factor and Gaussian mixture model, an improved dynamic identification algorithm is proposed to realize the automatic selection of threshold of abnormal data. Second, based on the random forest algorithm and least squares regression method, a dynamic filling algorithm for missing data is proposed. Depending on the length of missing data, it adaptively optimizes the filling algorithm to ensure filling accuracy and reduce running time. An integrated dynamic cleaning architecture is built through abnormal data identification and missing data interpolation. The data of the distribution network in a certain area of Hunan are used for example verification. The results show that the proposed method can realize accurate and automatic abnormal data detection and achieve a balance between the filling accuracy and speed of missing data in a distribution network. This has good engineering application value.