Network on chip (NoC) is a promising communication infrastructure for multiple cores on a chip to exchange data efficiently. In such NoC architecture, application mapping is the process of assigning tasks to the processing cores. An optimized application mapping technique enhances the performance of a chip and reduces the entire chip's energy consumption. The optimization of application mapping is essential in the design of NoC. In this study, the greedy algorithm is utilized as the first technique to place the maximum communicating tasks together to give the main algorithm a head start. Then, a meta-heuristic Cuckoo Search via Lévy flight is employed further to optimize the placement of tasks on the NoC cores. The greedy algorithm furnishes a relatively pre-processed base to the CSO, which eventually helps in the fast convergence of the main algorithm. The analysis of the results shows that the proposed algorithm outperformed the state-of-the-art techniques in NoC application mapping in terms of various performance metrics, such as communication cost, energy consumption, and average packet latency.