This paper presents a Hybrid Salp Swarm Algorithm with Particle Swarm Optimization (HSSA-PSO) to enhance optimization efficiency by addressing SSA's limitations in balancing exploration and exploitation. Based on the fusion of PSO's velocity updation capability, HSSA-PSO enhances solution quality, prevents premature convergence, and enhances exploitation efficacy. The proposed algorithm was validated using the CEC 2005 and CEC 2017 benchmark problem sets and practical engineering design problems. A comparison with state-of-the-art optimization techniques demonstrates that HSSA-PSO consistently achieves competitive and often superior results. The hybridization effectively alleviates stagnation at local optima and enhances the convergence rate, making it a good approach for complex optimization problems.