The micro-machining scale effect makes it challenging to forecast and control the process parameters of the micro-milling process, which makes the micro-flanking-milling of weak-rigidity micro-thin-walled parts prone to deformation. To determine the critical cutting parameters for chip formation in the micro-milling of curved thin-walled parts at the mesoscale, the strain-softening effect of titanium alloy during high-speed milling and the scale effect of mesoscale cutting were comprehensively considered and a finite element prediction model for curved micro-thin-wall micro-milling was established to determine the critical milling parameters for effective material removal. Based on the determined milling parameters, an experimental design of response surface optimization was carried out. Based on the response surface methodology, a data-driven quantitative model with milling process parameters as design variables and deformation amounts as response variables was established to reveal the influence mechanism of multiple milling process parameters on machining accuracy. Based on the process requirements for deformation control in the micro-milling of curved thin-walled structures, dynamic optimization of the milling process parameters was performed using an improved NSGA-III algorithm to obtain non-dominated solutions. A visual ranking and a determination of the unique solution were conducted using the entropy weight–TOPSIS method. Finally, micro-milling validation experiments were carried out using the optimal parameter combination. The optimal solution for the process parameters of the arc-shaped micro-thin-wall micro-milling of titanium alloy established by the institute provides a relevant reference and guidance for mesoscale arc-shaped thin-wall micro-milling.