Dry EDM is a modified technique with substituted gaseous medium instead of liquid dielectrics in the usual EDM phase. In this work, dry EDM attempt is made with copper electrode on Inconel 718 material using L27 orthogonal array with compressed air as a dielectric medium. This study takes into account process variables like gas dielectric pressure, pulse current, spark on time, and gap spark voltage. ANN models is developed using Feed-forward back propagation algorithms with trainlm, learngdm, MSE, transig as the training, learning, performance and transfer functions respectively for the Material Removal Rate (MRR), Tool Wear Rate (TWR) and Surface roughness (Ra) as a response characteristics. A good fitness is observed for the trained model. Experimental response values and the ANN predicted values is compared and found that the overall correlation coefficient was 0.94455 which shows good prediction accuracies and effectiveness of the model and ANN technique.