Rural electrification is a critical challenge in many developing countries, where conventional grid extension is often not feasible or cost-effective due to low load density and long distances. Hybrid AC/DC microgrids offer a promising alternative solution, providing a reliable and sustainable electricity supply to rural communities. This study presents a comparative analysis of four clustering techniques (hierarchical, k-means, fuzzy c-means, and gaussian mixture models) for optimizing cable routing by grouping loads in a low-voltage hybrid AC/DC microgrid in rural electrification areas. The proposed approach consists of several stages: (1) grouping loads into the clusters using four clustering techniques; (2) optimizing the radial topology in clusters of the microgrid by using minimum spanning tree (MST) and shortest path algorithms (SP); (3) balancing the three-phase system using mixed-integer linear programming (MILP); and (4) performing an economic analysis to evaluate the effectiveness of the four clustering techniques. The methodology is applied to a real case study of an island area in Cambodia, and the performance of a hybrid microgrid under different clustering configurations is compared. The results show that k-means clustering is the most cost-efficient solution for optimizing the topology of a hybrid AC/DC microgrid in rural Cambodia.