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Electricity demand is increasing daily due to the growth of industries. To meet this demand, it is essential to increase power generation, improve economic efficiency, and control pollutant emissions. A power system focuses on grid stability, which neglects economic efficiency and pollutant emissions. To overcome this problem, a Combined Economic and Emission Dispatch (CEED) problem is proposed. By solving the CEED problem, both the total cost and emissions, including NOx, SO2, and CO2, are minimized. The Lévy Opposition-Based Learning Bat Algorithm (LOBL-BA) is proposed to solve the CEED problem. The Bat Algorithm (BA) suffers from slow convergence and can get stuck in local optima due to its limited exploration. To address these limitations, three strategies are proposed: Firstly, the Random Inertia Weight improves search capability. Lévy Opposition-Based Learning is proposed to enhance the exploration-exploitation trade-off, thereby increasing robustness in tackling complex optimization problems. Finally, a Sine Function-driven Local Search is proposed to refine solutions and improve the ability to escape local optima. Finally, the improved LOBL-BA is tested on a 6-unit system with four different loads (150 MW, 175 MW, 200 MW, and 225 MW). The performance of the enhanced algorithm is assessed and compared with that of the following algorithms: BA, FPA, GWO, MVO, SCA, AOA, WSO, and RIME. The outcomes of the enhanced algorithm consistently achieve total costs and reduced emissions across all test cases.