Urban navigation poses a significant challenge due to the complexities of city layouts, which range from structured grid systems to organic, irregular road networks. This study aims to optimize navigation by integrating graph generation techniques with advanced pathfinding algorithms, creating a system that simulates and analyzes various urban layouts. The problem is formulated as a comparative analysis of algorithm performance across different graph structures representing cityscapes. A unified methodology is employed, combining graph generation algorithms such as Voronoi, Prim’s, Cellular Automata, Kruskal’s, and others, with pathfinding methods including Dijkstra’s, A*, and Greedy Best-First Search. The analysis evaluates the efficiency of these algorithms based on execution time, memory usage, and path length across multiple city layouts, supported by a multi-agent navigation framework. Observations reveal that certain algorithms outperform others in specific graph types, providing insights into the interplay between urban design and navigation efficiency. The results highlight the potential for algorithm-driven optimization in urban planning and autonomous navigation systems, with suggestions for further research focused on scalability and real-time applications.