This research investigates the comparison of the thrust force (Fz) and cutting torque (Mz) predictive models based on artificial neural networks (ANN). The models were developed based on three-level full factorial design of experiments conducted when drilling of Al6082-T6 alloy material with cutting speed, feed rate and tool diameter as the process parameters. The predictive ANN models of thrust force (Fz) and cutting torque (Mz) were developed using a multilayer feed forward neural network, trained using an error back propagation learning algorithm. The developed models are shown capable of predicting them (Fz and Mz) to a great level. The confirmation experiments provided favorable results with accuracy of 4% and 4.5%, for the thrust force (Fz) and the cutting torque (Mz) respectively. Furthermore the R coefficient for the prediction model of the thrust force is 0.99608 and 0.9946 for the cutting torque. As a result they can be considered as very accurate and appropriate for their prediction. This study promotes the better machining quality and the sustainability of the process that are achieved at the same time