Aiming at the problem that the traditional K-means clustering algorithm was difficult to adapt to underdeveloped overseas cities because of chaotic planning and unevenly distributed power loads, a method based on improved K-means clustering algorithm for the distribution network planning of underdeveloped overseas cities was proposed. Firstly, the impact of capacity margin on partitions was considered, and the weighting factor was added to improve the Euclidean distance. Secondly, the selection of power supply units were considered by the characteristics of the substations, and the distance between the clustering center point and the substation was calculated. Finally, a distribution network planning model was constructed with the most power supply units between stations and the smallest total Euclidean distance considering power distribution as the objective function. Taking the transformation of the distribution network in the Dhanmondi area of Dhaka, the capital of Bangladesh, as an actual engineering application example, the results shows that the average difference of load partitions obtained by the improved K-means clustering algorithm is reduced by 34.35% compared with the traditional method.