<p>The Beluga Whale Optimization (BWO) algorithm is a robust metaheuristic approach for addressing practical optimization challenges. However, BWO exhibits certain limitations, such as slow convergence and a tendency to become trapped in local optima. To overcome these drawbacks, an enhanced version of the algorithm, termed the Chaotic Bidirectional Beluga Whale Optimization (CBBWO), has been proposed. This improved variant integrates three key strategies: a vertical-horizontal adaptive cross strategy, multi-chaotic mapping strategy, and a bidirectional search strategy. To validate the performance of CBBWO, extensive evaluations were conducted using both classical benchmark functions, the CEC2017 and CEC2022 benchmark suites. Additionally, the algorithm was applied to 10 engineering problems and compared with 10 other state-of-the-art algorithms. To ensure the fairness and accuracy of the evaluation, comparative experiments were performed between CBBWO and the aforementioned algorithms with both sets maintaining identical iteration counts and function evaluation limits on classical benchmark functions and the CEC2017 benchmark suite. Furthermore, to comprehensively assess the superiority of CBBWO, its performance was benchmarked against three champion algorithms and several prominent multi-hybrid evolutionary algorithms. Finally, the CBBWO algorithm was successfully applied to four distinct maps for mobile robot path planning problems. Extensive experimental results and rigorous statistical analyses confirm that CBBWO demonstrates superior performance and practical applicability compared to other optimization algorithms.</p>