Thin-walled milling of aluminum alloys is widely used in aerospace and precision manufacturing but presents challenges such as vibration, deformation, and surface quality degradation. This study develops a Fuzzy-VIKOR-based multi-objective optimization framework to determine the optimal cutting parameters—cutting speed (Vc), feed per tooth (fz), and width of cut (ar)—that minimize surface roughness (Ra) and flatness deviation (FL) while maximizing material removal rate (MRR). A Full Factorial experimental design with 27 trials was conducted on Aluminum Alloy 6061 using a DMU50 5-axis CNC milling machine, and machining responses were measured using high-precision instruments. Results showed that a cutting speed of 150 m/min, feed per tooth of 0.05 mm/tooth, and width of cut of 1.0 mm provided the best trade-off, achieving Ra = 0.42 µm, FL = 7.6 µm, and MRR = 187.5 mm3/min. Sensitivity analysis confirmed that setting v = 0.5 in VIKOR ensures a balanced optimization. Compared to traditional approaches such as Taguchi and RSM, the Fuzzy-VIKOR method demonstrated superior robustness in managing uncertainty and conflicting objectives. The proposed optimization strategy enhances thin-walled milling performance and is applicable in aerospace, automotive, and high-precision manufacturing industries.