The increased complexity of modern power systems, driven by the integration of renewable energy sources and growing electricity demand, has led to optimized smart grids. Traditional grid management approaches are often insufficient in addressing the challenges of efficiency, reliability, and minimizing losses. This study evaluates the performance of various optimization algorithms in minimizing power losses, reducing voltage deviation, and improving convergence efficacy in power system optimization. Among the analyzed methods, Linear Programming (LP) exhibits the highest power losses of 220 kW and voltage deviation, making it the least effective approach. In contrast, Genetic Algorithm (GA), Traditional Particle Swarm Optimization (PSO), and Differential Evolution (DE) achieve moderate reductions in power losses (10–18%) and voltage deviation (12–20%). The Proposed Deep Reinforcement Learning-based PSO (DRL-PSO) and Improved PSO demonstrate enhanced performance, reducing power losses by 22–28% and improving voltage stability. The most efficient methods—MultiObjective PSO (MOPSO), Multi-Objective Wind Driven Optimization (MOWDO), and Multi-Objective Genetic Algorithm (MOGA)—achieve the lowest total losses (~140–160 kW) with a significant reduction of 30–35% and minimal voltage deviation (~0.01–0.015 p.u.), ensuring optimal system stability. While DRL-PSO requires longer convergence time due to its complexity, MOPSO, MOWDO, and MOGA exhibit the fastest convergence while maintaining superior optimization performance, making them suitable for real-time applications.