The African Vultures Optimization Algorithm (AVOA), a newly developed swarm-intelligence meta-heuristics motivated by the scavenging and hunting behaviors of African vultures in the wild, has lately been extensively applied in many different domains. However, the AVOA still possesses significant drawbacks in solving challenging applications, including poor convergence accuracy, a lack of exploration ability, and being prone to local optima. To alleviate these drawbacks, a novel approach entitled “Boosted Dynamic Chaotic Opposite Learning (BDCOL)” technique is proposed and incorporated with AVOA, termed BDCOL-AVOA, to improve the performance of the AVOA. This study employs boosted dynamic chaotic opposite points to initialize the population and generation updating rather than opposite points. In BDCOL approach, an iterative-based logarithmic decreasing weight factor is introduced to regulate the complexity of the search domain, while the Chaotic Opposite Learning (COL) technique is implemented to systematically explore the search domain employing non-linear scaling behavior with the goal of a good trade-off between intensification and diversification of the algorithm. To evaluate the efficacy of the BDCOL-AVOA, it is implemented on a set of 23 classical CEC’05, 10 complex CEC’21, and 12 recently developed CEC’22 test functions, and its outcomes are statistically and graphically tested against the AVOA, along with several other meta-heuristics. In addition, statistical tests, notably the Friedman, Wilcoxon rank-sum, and t-tests, have been employed to exemplify the dominance of the BDCOL-AVOA. Furthermore, the BDCOL-AVOA is applied to several real-world engineering applications. The experimental findings have substantiated that BDCOL-AVOA has immense potential for addressing real-world engineering design problems.