Metaheuristic computing intelligently combines the different concepts and iteratively explores and exploits the search space to determine the near-optimal solutions efficiently. Metaheuristic algorithms have emerged as a very powerful and suitable solution for solving complex problems in various application domains. To overcome the constraints in the traditional optimization algorithms for resolving the large-scale, stochastic, and non-linear problems, metaheuristic computing provides the global optimal solutions with iterative procedures. Moreover, it can address the hard problems, including the Non-deterministic Polynomial-time (NP) hard problem through the near-optimal solution with time efficiency. Nowadays, large-scale and time-sensitive real-time applications have been widely adopted in metaheuristic computing to efficiently handle the abundance of data and optimize the computations with a good solution over the numerous feasible solutions. In future, the metaheuristic computing has tackled the shortcomings in the analytical methods from the perspective of solving the optimization problem. To widely ensure the optimization for the different application domains such as cloud computing, networking, fog computing, intrusion detection, and machine learning, metaheuristic computing has been increasingly used due to its less computational effort and ability to guide in the search space.