In the present machining environment, application of nanofluids in metal cutting operations plays a vital role to improve machinability and efficiency of the machine tool. To reduce environmental hazards, minimal fluid application is more suitable. The preparation of suitable nanofluids is still a difficult task for metal cutting industries. The selection of nanoparticle and cutting fluids are not so easy because its properties will affect the performance of nanofluid preparation and performance. Titanium (Ti-6Al-4V) alloy is used as a work material because of its inherent properties. Uncoated carbide tool is used to cut the work material because of its less cost with good hardness capability. To find optimal machining condition and prediction, optimal output responses are very useful for the metal cutting process to enhance the machine tool performance. In the present research work, graphene nanoparticle was selected because its high thermal conductivity at elevated temperatures, and vegetable oils (soybean oils ) with high viscosity index to reduce tool wear, surface roughness, temperature, and cycle time. A nanofluid based minimal fluid application with optimization using GRA, PCA, and RSM proved its best. All three optimization process gave very close valves to each other. Since this research is a multi-objective, these developed models using Response Surface Methodology (RSM), Grey Relational Analysis (GRA), and Principal Component Analysis can be used for evaluation of surface roughness, cutting force, tool wear, temperature, and cutting time as well.