One well-known optimization problem in computational science and operations research is the Traveling Salesman Problem (TSP). The issue can be expressed as follows: The goal is to find the shortest route, given a set of cities and the geographical distance between each pair of cities, that makes exactly one stop at each city and then returns to the initial location. An NP-hard issue is TSP. This indicates that, as the number of cities rises, there is currently no effective method that can handle every instance of the problem rapidly (in polynomial time). Numerous practical uses for TSP exist, including DNA sequencing, tour scheduling, delivery truck routing, and more. We have proposed the use of nature inspired bat algorithms to solve TSP. Bats use sound waves and echoes to find prey and obstacles. This process is known as echolocation. Every virtual bat in the Bat Algorithm stands for a possible fix for an optimization issue. We have given a near optimal and fast converging solution to this combinatorial problem. We have used Google Colab software to simulate the results. Moreover, we have found the relation between other parameters like number of cities and number of iterations, α which controls the loudness of the bat’s emitted sound to get the best possible result using this algorithm which was not done in any previous studies.