The advancement in global energy management has directed to the broadening of more sophisticated and optimized models, which are being integrated to address the complexities of economic and energy distribution within grid network systems. The approved set of environmental factors has also contributed to the improvement of a synthesis grid structure, which helps reduce operational costs. This prime focus in this paper is the designing and implementation of an advanced hybrid intelligent algorithm to tackle the issue of energy economic management. The proposed algorithm is the Random Oppositional Invasive Weed Runner Root Optimization (ROIWRRO), which draws its intelligence from the biological behaviour of the runner plant. The plant’s self-regulated performance inspires the development of efficient strategies for exploring and managing grid systems. The proposed optimization framework demonstrates the potential to effectively eliminate inefficiencies when compared to conventional methods. Two standard bus system datasets with 6 and 15 configurations are used for the validation of the results of the ROIWRRO. In comparison, the routine of the hybrid performance produces superior optimal results.