As the energy transition accelerates worldwide, managing the intricacies of power flows is becoming a major challenge, particularly for complex grid systems like the Nordic grid. This paper details a comparative analysis of metaheuristic optimization algorithms–specifically, Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), and Grey Wolf Optimization (GWO)–to optimize power flow across Nordic pricing areas. Utilizing the Reduced Nordic 44 Pricing Area Model (Reduced N44 PAM) along with the IEEE 14, 39, and 118 bus benchmark test systems, this study evaluates the effectiveness of these algorithms in minimizing power losses and enhancing the efficiency of the power transmission network. The analysis is focused on three scenarios of the Reduced N44 PAM which are a base case, a light load case, and a heavy load case. The comparative results show that GWO outperforms both PSO and CSA, achieving up to 11.5% better loss minimization in the base case and showing faster convergence speeds across all scenarios. This study provides insights into the potential of metaheuristic algorithms to significantly enhance power flow efficiency and suggests the broader applicability of these techniques in power systems for robust grid management.