The paper proposes a novel hybrid optimization algorithm, DA-PSO, which integrates a modified Dragonfly Algorithm (DA) with Particle Swarm Optimization (PSO) to enhance solution accuracy and convergence speed. To evaluate DA-PSO's performance, extensive experiments were conducted on a comprehensive suite of 23(twenty-three) established benchmark functions, encompassing unimodal, multimodal, separable, and non-separable problems. The Proposed hybrid approach mitigates the limitations of individual algorithms, combining the exploration capabilities of DA with the exploitation Capability of PSO. DAPSO exhibited a notable enhancement in solution quality compared to the original DA and PSO algorithms, demonstrating the effectiveness of the hybrid approach. Experimental results and statistical analysis confirm the effectiveness of the proposed hybrid algorithm in addressing complex optimization problems.