The Slime Mould Algorithm (SMA) has effectively solved various real-world problems such as image segmentation, solar photovoltaic cell parameter estimation, and economic emission dispatch. However, SMA and its variants still face limitations when dealing with low-dimensional optimization problems, including slow convergence and local optima traps. This study aims to develop an optimized algorithm, the Merged Slime Mould Algorithm (MSMA), to overcome these limitations and improve performance in low-dimensional optimization tasks. Additionally, MSMA introduces a novel approach by merging the Adaptive Opposition Slime Mould Algorithm (AOSMA) and the Smart Switching Slime Mould Algorithm (S2SMA), simplifying the hybridization process and enhancing optimization performance. MSMA eliminates the need for multiple initializations, avoids memory-switching requirements, and employs adaptive and smart switching rules to harness the strengths of both algorithms. The performance of MSMA is evaluated using the CEC 2005 benchmark and ten real-world applications. The Wilcoxon rank-sum test verifies the effectiveness of the proposed approach, with results compared to various SMA variations and related optimization methods. Numerical findings demonstrate superior fitness values achieved by the proposed strategy, while statistical results indicate MSMA's outperformance with a rapid convergence curve.