The paper introduces a new optimization algorithm called Chaotic Mountain Gazelle Optimizer (CMGO), which is an enhanced version of the Mountain Gazelle Optimizer (MGO). MGO is a swarm-inspired meta-heuristic algorithm known for its rapid convergence and accuracy, but it faces issues with premature convergence and getting stuck in local optima. To address these limitations, CMGO incorporates ten distinct chaotic maps to improve the algorithm's performance. These chaotic maps help in enhancing the exploitation and diversification phases, preventing premature convergence, and avoiding local optima. The effectiveness of CMGO is evaluated using benchmark functions from CEC2005 and CEC2019, as well as four real-world engineering problems. Statistical tests, including the t-test and Wilcoxon rank-sum test, are conducted to validate the superiority of CMGO over other well-known algorithms such as PSO, GWO, DE, LSHADE, CMAES, FFA, WSO, and MGO. The results show that CMGO outperforms these algorithms in most cases, demonstrating its robustness and efficiency in solving complex optimization problems. The study concludes by highlighting the potential of CMGO in addressing a wide range of optimization challenges and suggests future research directions.The paper introduces a new optimization algorithm called Chaotic Mountain Gazelle Optimizer (CMGO), which is an enhanced version of the Mountain Gazelle Optimizer (MGO). MGO is a swarm-inspired meta-heuristic algorithm known for its rapid convergence and accuracy, but it faces issues with premature convergence and getting stuck in local optima. To address these limitations, CMGO incorporates ten distinct chaotic maps to improve the algorithm's performance. These chaotic maps help in enhancing the exploitation and diversification phases, preventing premature convergence, and avoiding local optima. The effectiveness of CMGO is evaluated using benchmark functions from CEC2005 and CEC2019, as well as four real-world engineering problems. Statistical tests, including the t-test and Wilcoxon rank-sum test, are conducted to validate the superiority of CMGO over other well-known algorithms such as PSO, GWO, DE, LSHADE, CMAES, FFA, WSO, and MGO. The results show that CMGO outperforms these algorithms in most cases, demonstrating its robustness and efficiency in solving complex optimization problems. The study concludes by highlighting the potential of CMGO in addressing a wide range of optimization challenges and suggests future research directions.