The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem, that has gained popularity over the past few years because of its broad applicability in service, logistics, and industrial optimization processes. The search for the optimal path, whose result is minimizing the total cost or distance, has always been a computationally intensive and difficult task.This paper proposed a new hybrid meta-heuristic algorithm, Genetic Algorithm and Discrete Marine Predators Algorithm (GA-DMPA), for solving the TSP. In the first phase (exploration), the genetic algorithm is used to generate candidate tours per iteration. The second phase (exploitation) is the stage in which the Discrete Marine Predators Algorithm further narrows down on these candidate solutions to improve local search and increase convergence to the global optimum. This hybridization can be used to avoid premature convergence and to improve the quality and strength of the solutions. The practicality of the proposed GA-DMPA is proved by applying it to 20 real Indian cities and a benchmark function. The outcomes of the experiment compared with GA and DMPA algorithms, and the introduced hybrid GA-DMPA algorithm give better results. The results are summarized and present in form of figures and table.