The present study provides predictive models for the functional relationship amongst the input and output variables of Electrical Discharge Machine (EDM) environment. The parametric optimization of this process can be regarded as a multi-objective task. No particular parametric combination of input parameters can offer the maximum Material Removal Rate (MRR) and a better surface finish concurrently, due to its conflicting nature. Hence, a Multi-objective optimization approach has been attempted for the best process parametric combinations by modelling EDM process using of Artificial Neural Networks (ANN). It provides an optimized input data set to EDM system and the results show an improvement with a better productivity, a reduced material removal time and product cost at the material removal rate and surface finish. Extensive experiments have been accompanied with a wide range of machining settings, for modelling and, then, for validating the model. The model is quite capable of predicting the MRR and surface roughness. Also, it is found that the quality of the surface decreases as MRR increases. The maximum MRR obtained is 51.58 mm3/min with the surface finish of 0.1466 μm. © 2016 Jordan Journal of Mechanical and Industrial Engineering. All rights reserved.