The optimal operation of any electrical power system is considered a crucial aspect in modern day optimization. The optimal power flow problem is widely known as an effective tool to plan the operation of these systems. However, due to the nonlinearity of variables such as generation cost and power losses, computation is difficult with conventional optimization methods. Therefore, various metaheuristic algorithms are implemented to solve the Optimal Power Flow (OPF) problem and overcome the possible drawback of conventional methods. This systematic review will explore various popular metaheuristic techniques that are employed to solve the optimal power flow problem such as Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) along with various newly introduced and emerging optimization techniques. Diverse techniques were reviewed for optimizing the OPF problem, however, deployment of artificial bee colony optimization algorithm has proved its popularity due to its fast computational speed and rate of convergence as compared to the previously established algorithms based on the reported qualitative data from existing studies. Furthermore, the qualitative data analyzed presented a higher frequency of studies adopting artificial bee colony optimization algorithm, presenting accurate results in critical power system aspects such as generation cost, real power losses, and total emissions. Additionally, this research provides the pathway for future research in improving the performance of metaheuristics when applied to the optimal power flow problem.