A time-optimal 3-5-3 polynomial interpolation trajectory planning approach using the Improved Adaptive Inertia Weighted Particle Swarm Optimization (IAIWPSO) algorithm is developed for the time-optimal trajectory planning challenge of a robotic arm. The method enhances the algorithm by incorporating chaotic mapping for population initialization, an adaptive S-curve for nonlinearly decreasing inertia weight, and a trigonometric function for the asynchronous learning factor, thereby significantly improving convergence accuracy and speed. The experimental findings indicate that the strategy has considerable benefits in minimizing robot operational time and improving production with increased efficiency and stability.