This paper offers a Hybrid MFO-GWO algorithm, by merging exploration ability of Moth-Flame Optimization (MFO) with exploitation strength of the Grey-Wolf Optimizer (GWO). A compatible transition factor (TF) dynamically bring into balance exploration and exploitation to enhance performance. The algorithm was tested on 23 benchmark functions, attaining better results in 14 cases equated to single MFO and GWO. The results confirm its improved precision, stability, and convergence speed, making it a strong candidate for global optimization tasks.