In this paper, we present a maze-solving system that integrates image preprocessing techniques, graph-based modelling, and pathfinding algorithms to identify the shortest paths through a maze. Preprocessing steps, including adaptive thresholding and median filtering, ensure accurate graph construction from images. The graph is built using a grid-based approach, balancing computational efficiency with structural accuracy. Three algorithms, breadth-first search, A*, and ant colony optimization (ACO), are implemented and compared. Results show that breadth-first search offers the fastest solution for smaller, simple mazes, while A* minimises node exploration, providing the shortest paths and excelling in complex environments. ACO, though slower, demonstrates adaptability in scenarios where dynamic pathfinding is required. The system’s performance illustrates its potential for search and rescue applications, where fast and efficient pathfinding is essential. Future work will focus on optimising preprocessing times, refining algorithm performance, and exploring real-time data integration to enhance the system’s applicability in real-life scenarios.