Merging Opposition Theory with the Mantis Search Algorithm pops up as a new idea to boost global optimization, aiming to speed up the convergence toward a solution and sharpen its accuracy. In most engineering problems, better optimization methods are acutely needed. The work compared this new combo with more typical techniques over a mixed bag of benchmark tests. The results prove that tossing Opposition Theory with Mantis search algorithm gives a noticeable lift, providing faster convergence and more accurate results popped out of the analysis. This paper introduces the Opposition-Based Enhance Mantis Search Algorithm (EMSA) and explores its application in single-objective optimization. One observation that these upgrades might work well in tricky areas like drug discovery or treatment planning. Beyond just adding a theoretical feather in the cap, such advances offer practical ways to improve decision-making. To assess the effectiveness of this modification, the algorithm is evaluated using the CEC-20 and CEC-22 benchmark functions. The results demonstrate that the proposed enhancements yield meaningful improvements. Ultimately, the study adds a fresh twist to our understanding of optimization while showing that blending innovative algorithms with trusted methods can open new paths for tackling complex challenges, even if the path isn’t perfectly smooth all the time.