A Chaotic-Based Mountain Gazelle Optimizer (CMGO) is proposed to enhance the performance of the Mountain Gazelle Optimizer (MGO) algorithm. MGO, a nature-inspired meta-heuristic, is known for its rapid convergence and accuracy but suffers from premature convergence and getting stuck in local optima. To address these issues, CMGO integrates ten distinct chaotic maps to improve exploration and exploitation capabilities, prevent premature convergence, and enhance the algorithm's ability to balance exploration and exploitation. The chaotic maps are used to generate diverse search spaces and improve the algorithm's ability to find global optima. The CMGO algorithm was evaluated using benchmark functions from CEC2005 and CEC2019, as well as four real-world engineering design problems. Statistical tests, including t-test and Wilcoxon rank-sum test, confirmed that CMGO outperforms existing algorithms in terms of convergence speed, accuracy, and robustness. The algorithm was also tested on engineering design challenges such as tension/compression spring design, gear train design, speed reducer design, and three-bar truss design. Results showed that CMGO provides better solutions than other optimization algorithms in these applications. The CMGO algorithm demonstrates superior performance in solving both unimodal and multi-modal optimization problems. It effectively balances exploration and exploitation, leading to faster convergence and better solutions. The algorithm's performance was validated through extensive numerical experiments and statistical analysis. The results indicate that CMGO is a promising approach for solving complex optimization problems in various fields, including engineering and data science. Future research could focus on applying CMGO to other domains and improving its efficiency in handling complex, multi-modal functions.A Chaotic-Based Mountain Gazelle Optimizer (CMGO) is proposed to enhance the performance of the Mountain Gazelle Optimizer (MGO) algorithm. MGO, a nature-inspired meta-heuristic, is known for its rapid convergence and accuracy but suffers from premature convergence and getting stuck in local optima. To address these issues, CMGO integrates ten distinct chaotic maps to improve exploration and exploitation capabilities, prevent premature convergence, and enhance the algorithm's ability to balance exploration and exploitation. The chaotic maps are used to generate diverse search spaces and improve the algorithm's ability to find global optima. The CMGO algorithm was evaluated using benchmark functions from CEC2005 and CEC2019, as well as four real-world engineering design problems. Statistical tests, including t-test and Wilcoxon rank-sum test, confirmed that CMGO outperforms existing algorithms in terms of convergence speed, accuracy, and robustness. The algorithm was also tested on engineering design challenges such as tension/compression spring design, gear train design, speed reducer design, and three-bar truss design. Results showed that CMGO provides better solutions than other optimization algorithms in these applications. The CMGO algorithm demonstrates superior performance in solving both unimodal and multi-modal optimization problems. It effectively balances exploration and exploitation, leading to faster convergence and better solutions. The algorithm's performance was validated through extensive numerical experiments and statistical analysis. The results indicate that CMGO is a promising approach for solving complex optimization problems in various fields, including engineering and data science. Future research could focus on applying CMGO to other domains and improving its efficiency in handling complex, multi-modal functions.