The use of nanofluid in lubrication during machining of advanced engineering ceramics has been found to be highly efficient and eco-friendly. This work involves experimental investigation of grinding Alumina (Al 2 O 3 ) ceramic to determine the effect of the grinding variables. The grinding variables considered include depth of cut, feed rate, type of diamond wheel, and lubrication type. Moreover, the response parameters considered include grinding power, coefficient of friction, and surface quality. The responses obtained during the experiments were used to develop a fuzzy logic prediction model. The findings from this work can be concluded as follows: (a) The depth of cut and feed rate have direct proportional relationship with the grinding power and coefficient of friction. (b) The metallic bonded diamond wheel was found to have higher machining efficiency than the resinoid bonded one. (c) Higher number of diamond grits produces lower frictional coefficient. (d) The carbon nanotube based nanofluid when used in the minimum quantity lubrication (MQL) process proffers better lubrication capability than conventional flood cooling system. (e) The developed fuzzy logic models were found to have high prediction accuracies of 97.22%, 98.60%, and 96.8%, respectively, for grinding power, grinding force ratio, and surface roughness. K E Y W O R D S grinding, alumina, nanoparticles, carbon nanotube, fuzzy logic, alumina