This study investigates the application of particle swarm optimization (PSO) algorithm in optimizing the force uniformity of personalized 3D-printed insoles, aiming to enhance the comfort and functionality of the insoles. Traditional insole designs often lead to uneven force distribution due to fixed lattice materials and structures, particularly in critical areas such as the forefoot and heel, which can result in health issues. This research proposes an optimization model that combines the PSO algorithm with a variable density algorithm, enabling dynamic adjustments to the support capabilities of different regions of the insole to achieve uniform force distribution. The results indicate that after optimization with the PSO algorithm, the force distribution of the insoles has significantly improved, with pressure peaks effectively dispersed in critical areas. Furthermore, this research validates the effectiveness of the optimization method through 3D printing technology, providing a theoretical foundation for the design and optimization of personalized 3D-printed insoles. It demonstrates that the combination of the PSO algorithm and variable density methods can effectively improve insole performance, showing promising application prospects.