This paper studies the analysis of the Euclidean Traveling Salesman Problem (ETSP), a variant of the Traveling Salesman Problem (TSP), through application of computational intelligence techniques. The TSP is a classical NP-hard combinatorial optimization problem. The ETSP specifically considers cities as points in Euclidean space where distances satisfy the triangle inequality. Heuristic frameworks for algorithmic performance evaluation of genetic algorithm, greedy constructive method, local search operators using artificial intelligence on synthetic and real-life geographic data. The results demonstrate that neuro-evolutionary strategies can yield tours that are close to optimal, particularly as the dimension of the problem increases. The balance between quality of solution, temporal complexity and scalability in an artificial intelligence optimisation framework are further explored. The open-source implementation serves as a consistent platform for advancing intelligent routing in automated logistics and spatial reasoning.