Biodegradable lubricant based on the blend of various vegetable oils with different nano friction modifier in combination is designed using computational intelligence technique and experimentally tested. A database is developed from the published literature on vegetable oil-based biodegradable lubricants with nano friction modifier as additives. The vegetable oils considered are coconut oil, castor oil and palm oil, whereas the friction modifiers taken into account are multi-walled carbon nanotubes and graphene. The database is used to develop artificial neural network models for predicting anti-wear properties of the lubricant expressed in terms of coefficient of friction measured through four-ball tester and pin-on-disk technique. The neural network model is used for data analytics to increase the understanding of such lubricant systems using simulation studies. Here the anti-wear effect of the vegetable oils and friction modifiers individually and also in combination is studied. The developed models are used for design optimization using a genetic algorithm. The optimal solutions are analyzed to study the role of various constituents for achieving superior performance of the lubricant. The developed lubricant is experimentally tested using four-ball tester and pin-on-disk techniques.