The total cost of electricity generation is minimized while fulfilling the total load demand and considering all constraints in the Combined Economic Emission Dispatch (CEELD) problem. Electricity generation from fossil fuel negatively impacts the environment. Therefore, various optimization techniques have been deployed for the CEELD problem. In the literature, the Modified Random Search Particle Swarm Optimization (MRSPSO) and Flower pollination algorithm (FPA) are used as a solution for CEELD known as Combined Economic Emission Load dispatch problem. However, MRSPSO is easy to settle into local optima in high-dimensional space and delivers a low convergence rate in the iterative process, whereas in the FPA, the diverse population make it prone to being limited to the local optima. Thus, in order to overcome these limitations, in this paper, we have hybrid the FPA and MRSPSO algorithm that improves the convergence rate to meet the optimal solution. Initially, we have implemented MRSPSO and FPA algorithm; after that, combined it for CEELD. The experimental results were performed in MATLAB. The experimental results show that the hybrid approach gives better results as compared to the MRSPSO and FPA. Thus, the proposed technique is efficient and can be deployed for real-time CEELD problem.