The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem with significant applications in logistics, transportation, and network design. Efficiently solving this problem requires a careful balance between exploration and exploitation while addressing challenges such as premature convergence and stagnation in local optima. To tackle these issues, numerous algorithms from different perspective have been designed and developed. Among them, Simulated Annealing (SA) is a widely used meta-heuristic approach for solving TSP due to its ability to escape local optima and explore a broad solution space. However, conventional SA can still become trapped in local minima, leading to suboptimal solutions. In this paper, we propose an enhanced SA algorithm that incorporates self-escape mechanism to improve the solution quality of the TSP instances. The self-escape mechanism dynamically identifies trapped routes and facilitate better exploration and diversification. Specifically, the self-escape mechanism introduces a local search refinement process, allowing solutions to effectively escape local optima. Simulation results on benchmark TSP instances demonstrate that the proposed algorithm outperforms conventional SA in terms of solution accuracy. The findings suggest that self-escape mechanism can significantly enhance the effectiveness of SA by preventing premature convergence in complex optimization problems.