Breadth-First Search (BFS) is a foundational graph traversal algorithm, it’s systematic layer-by-layer exploration of nodes makes it invaluable for a variety of domains, including transportation networks, social network analysis, and artificial intelligence. However, traditional BFS implementations face challenges when dealing with large-scale graphs due to memory limitations and inefficiencies in handling massive datasets. This project addresses these challenges by integrating BFS with a CSV-based data storage system, enabling efficient traversal of large graphs without relying on a traditional SQL database or requiring the entire graph to be loaded into memory. The graph data, comprising nodes and edges, is stored in CSV files, which act as lightweight and accessible storage. The implementation is memory-efficient due to the use of Pandas DataFrames for handling CSV data and NetworkX graphs for traversal. Additionally, the integration of a machine learning model from Scikit-learn, a memory-efficient library, ensures effective prioritization of edges without excessive computational overhead. In this project, we address a key limitation of the traditional Breadth-First Search (BFS) algorithm: its inability to consider edge weights during traversal. It is unsuitable for scenarios where varying edge weights significantly impact the traversal outcome, such as in shortest-path calculations for weighted graphs. To overcome this drawback, our project integrates a machine learning (ML) model to analyze and prioritize edges based on their weights, effectively augmenting BFS for weighted graphs. By leveraging CSV-based storage and combining it with an ML-driven edge prioritization mechanism. This project offers a scalable solution for managing and analyzing large, weighted graphs. This combination ensures that the navigation system not only computes the shortest path but also suggests the most practical and efficient routes tailored to user preferences or constraints.