The demand for sustainable micro-manufacturing technologies is growing with their increasing application in micro and nanoelectromechanical systems. The micro-manufacturing techniques utilizing physical or chemical energy have adverse effect on environment. Utilization of biological energy could be a benign alternative approach. In the exhaustive list of micro-manufacturing processes, biomachining is the only process that utilizes biological energy by making use of the energy pathway of microorganisms for removing metals. Biomachining has been proven to present numerous advantages such as being environmental friendly, low consumption of energy, and no recast layer/heat affected zone generation. Despite all the advantage, biomachining has not been commercialized because of the common shortcomings reported i.e. a low metal removal rate and increased surface roughness after machining. The aim of this study is to develop an empirical model to optimize and predict machinability and surface roughness of polycrystalline copper in a biomachining process using response surface methodology. The correlation between different processes has been investigated and central composite design (CCD) was employed to develop an empirical model based on the correlation between input variables (process parameters) and output responses (MRR and change in Ra). The optimum values of selected parameters were predicted and verified by performing a series of experiments.