The optimal power flow (OPF) problem is a highly nonlinear and complex multi-dimension optimization problem, especially with the increased penetration of uncertain renewable energies (RES). In this line, this paper presents the Hybrid Brown-Bear and Hippopotamus Optimization Algorithms with Quasi-Opposition-Based Learning (HBOA-QOBL) to enhance multi-dimension OPF solution. The algorithm combines the strengths of Brown-Bear optimizer, which excels in exploration and adaptive search mechanisms, and the Hippopotamus optimizer, known for its social behavior modeling and localized search strategies. By integrating QOBL, the HBOA-QOBL improves exploration through the generation of quasi-opposite solutions, allowing for a wider search of the solution space and reducing the risk of premature convergence. Adaptive search mechanisms embedded in HBOA-QOBL enhance exploitation by dynamically adjusting search behaviors during iterative power dispatch tuning, enabling improved fine-tuning of generation schedules and voltage profiles. The effectiveness of the proposed method is evaluated on the IEEE 30-bus, 57-bus, and 118-bus test systems for multiple dimension OPF objectives, including fuel cost minimization, emission reduction, power loss reduction, voltage deviation minimization, reactive power loss reduction and the voltage stability indicator (L-index). Simulation results indicate faster convergence compared to conventional techniques, achieving near-optimal solutions within 200 iterations, with a standard deviation of 63.8%, demonstrating superior technical and economic performance relative to previous research. Key convergence parameters such as population size, maximum iterations, and learning factor are explicitly tuned to enhance both exploration and exploitation. Simulation results confirm that HBOA-QOBL outperforms conventional optimization techniques in terms of solution quality, convergence speed, and stability, establishing significant improvement in the technical and economic issues.