In the present investigation two smart prediction tools, namely the general regression neural network (GRNN) and multiple regression analysis (MRA) models were developed to predict and compare some of the key machinability aspects like average kerf width, average surface roughness and material removal rate in the wire electrical discharge machining process of titanium grade 6. Pulse-on time, pulse-off time, wire feed and wire tension were considered as machining variables to develop the predictive model. In order to curtail cross-validation error in GRNN, optimized kernel bandwidth was utilized using the grid search method. The neural network and regression models were trained, validated and tested with measured data. A mathematical model was developed using multiple regression analysis. The ANOVA test was also conducted to determine the significant parameters affecting the responses. The results indicated that the predicted responses lie within ± 5% and ± 10% error for GRNN and MRA, respectively, which suggests that the GRNN model is more reliable and adequate than the regression model. A comparative study with previous research work was also done to confirm the novelty along with application potential of the proposed model.