<![CDATA[Transportation network optimization is a crucial aspect of urban planning, logistics, and mobility management. Graph theory provides a mathematical framework for modeling and improving transportation systems by representing networks as nodes and edges, enabling efficient route planning, traffic flow optimization, and resource allocation. This study explores the application of graph theory in optimizing transportation networks, focusing on key algorithms such as Dijkstra’s Algorithm, Minimum Spanning Tree (MST), and Network Flow Models. Using a qualitative research approach, this paper examines recent advancements, case studies, and theoretical perspectives in transportation optimization. The study highlights how graph-based methods enhance efficiency, reduce congestion, and improve cost-effectiveness in various transportation domains, including urban traffic management, public transit scheduling, air traffic control, and logistics networks. Additionally, it discusses computational challenges and potential solutions, particularly in large-scale networks requiring high-performance computing and artificial intelligence integration. Through a comprehensive literature review, this research identifies critical trends, such as the fusion of graph theory with AI, IoT, and blockchain technologies, which contribute to real-time data-driven decision-making in smart cities. The findings suggest that graph theory remains a fundamental tool for designing and optimizing resilient and sustainable transportation networks. The paper concludes with recommendations for future research, emphasizing the need for interdisciplinary approaches and emerging technologies to further enhance transportation network optimization.]]>
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