<p>This study is an attempt to improve the recently introduced American Zebra Optimization Algorithm (AZOA), which is inspired by the leadership dynamics and scavenging behaviour of American zebras in nature. Although AZOA demonstrates strong exploration capability, it suffers from certain limitations, such as weak exploitation ability and a tendency to become trapped in local optima when dealing with complex optimization problems. To alleviate these challenges, a novel strategy called Enhanced Opposition-Based Learning (EOBL) is suggested and integrated with the AZOA framework. The EOBL mechanism extends the traditional opposition-based learning by incorporating a degree of controlled randomness, aiming to achieve a better balance between exploration and exploitation during the search process. Consequently, an improved algorithm termed the Enhanced Opposition-Based American Zebra Optimization Algorithm (EOBAZOA) is proposed to enhance the performance of the standard AZOA. The effectiveness of EOBAZOA has been validated through extensive experimentation on both classical benchmark functions from CEC2005 and recent test suites from CEC2022, in addition to a set of real-world engineering design problems. Furthermore, rigorous statistical analysis, such as the t-test has been conducted to assess the robustness and reliability of the results. The experimental findings confirm that the proposed EOBAZOA approach achieves superior performance than other cutting-edge optimization algorithms in both benchmark and real-world engineering problem scenarios.</p>