In the process of identifying low-voltage outage areas in distribution networks, missing or false reports from medium-voltage distribution terminals often make it difficult to accurately determine the actual outage zones. To address this problem, this paper proposes a dynamic fuzzy Bayesian network-based outage area identification method for distribution networks. By integrating relevant data from both the user side and distribution terminals, and utilizing the typical topology of medium and low-voltage distribution networks, a dynamic Bayesian network (DBN) is constructed to infer the probability of outages occurring in various regions. Based on this initial inference, a verification step is conducted to determine whether results need to be corrected. If correction is required, the inference result is fed into a fuzzy reasoning system, where it is further refined using membership functions and inference rules, followed by defuzzification, to ultimately deduce the most probable outage area. An analysis of the actual fault data from a city’s distribution network show that, even with a 10% rate of simulated information loss, the accuracy of the model is 83.59 %, demonstrating its strong judgment accuracy under incomplete information conditions.