<![CDATA[The increasingly complex growth of urban mobility requires an analytical approach that can optimize transportation networks effectively and sustainably. Graph theory is one of the mathematical methods widely used in modeling road network structures and analyzing inter-node connectivity to obtain more efficient routing and network optimization solutions. This study aims to systematically review the development of graph theory application in urban transportation network optimization through the Systematic Literature Review (SLR) method. The review was conducted following the PRISMA 2020 protocol for articles published between 2020 and 2025. A total of ten articles met the inclusion criteria and were analyzed in depth. The review results show that graph algorithms such as Dijkstra, Bellman–Ford, Floyd–Warshall, Minimum Spanning Tree (MST), Graph Neural Network (GNN), and the hybrid Dijkstra–A* method can improve route efficiency, reduce travel time, improve navigation accuracy, and strengthen congestion prediction capabilities. In general, graph theory has proven to be an effective and adaptive approach in supporting urban transportation network planning and management. Further research is recommended to integrate graph theory with real-time traffic data and artificial intelligence technology to improve the accuracy and responsiveness of modern transportation systems.]]>
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