This paper is a comparative between classical, hybrid and heuristic shortest path algorithms. Shortest path algorithms are necessary in graph theory and has different applications in operations research, artificial intelligence, networking, and transportation. In dynamic or huge graphs, the deterministic correctness and limitations of classical algorithms such as Dijkstra's and Bellman-Ford have been investigated. The current research examined the flexibility and scalability of heuristic techniques like Ant Colony Optimization, A*, and Genetic Algorithms. Furthermore, examined the potential to improve and enhance the accuracy of hybrid algorithms that include characteristics from both categories has been studied. This work employs important evaluation standards, for instance, optimality, time complexity, and scalability, to inspect the theoretical underpinnings, practical applications, and performance compromises of each strategy.