Internal diamond burnishing is a prominent solution to produce surface finishing for interior holes. This work aims to propose a novel diamond burnishing process, in which an integrative lubrication using the Vortex tube and liquid CO2 is applied. Three key process parameters, including the spindle speed (S), feed rate (f), and burnishing depth (D) are optimized to decrease the surface roughness (SR) and improve the Vickers hardness (VH). The Box-Behnken method is applied to conduct the burnishing experiments. The artificial neural network (ANN) is used to develop burnishing response models, while the entropy method is utilized to compute the weights. The optimal solution is determined using the multiple-objective particle swarm optimization (MOPSO) algorithm. The results indicated that the optimal outcomes of the S, D, and f were 630rpm, 0.12mm, and 0.04mm/rev., respectively. The SR was decreased by 60.9%, while the VH was increased by 10.2% at the optimal solution. The outcomes could be applied to practical diamond burnishing to enhance the surface quality of the internal holes. The optimizing technique could be used to present the non-linear data and obtain optimal global results.