Real life engineering optimization issues are frequently discontinuous, highly nonlinear, and nonconvex in nature. Traditional optimization methods, which are mostly derivative based, either fall short of providing the desired solution for such issues or do so only after easing the nonlinearities. Hence, metaheuristic techniques are becoming sufficiently popular in the research community throughout time owing to their liberal nature in solving different optimization issues. Among different engineering problems, Load Flow Analysis (LFA) problem is an intricate, nonlinear optimization problem. In the light of this, an Adaptive Particle Swarm Optimization aided with Levy Flight (LF) technique has been modified by fusing the self-pollination property of the Flower Pollination Algorithm (FPA) to propose the Modified APSOLF (MAPSOLF) technique in this study for addressing the challenging nonlinear LFA problem. The performance of the proposed technique is further compared with the basic form of PSO and the LF motivated Adaptive PSO (APSOLF) techniques in terms of their convergence characteristics. Furthermore, the sturdiness and steadiness of proposed technique is well justified with the help of statistical parameters along with the nonparametric statistical test namely the Wilcoxon test. All simulated test results show the superiority of the proposed technique among all the techniques in study. In addition, it is important in mentioning that all the simulations are performed at MATLAB software platform on the standard IEEE 5 and 14 bus test systems.