Noise is one of the most prevalent sources of environmental pollution and is considered as the second largest pollution after air pollution. As there are many factors which contributes to noise pollution, the major contributor among all being the vehicular traffic noise. It is considered as one of the most invasive type of pollution and often the most intrusive of all. The trend of noise pollution modelling varies from the smart result of classic regressive models to the performance of many assessment models based on mathematical expression, genetic algorithms and neural networks. In this study, feed forward back propagation neural network has been developed to predict vehicular traffic noise in urban area. The study area is an ancient trade town having connections with major districts and states. NH-66 road stretch with 6 sampling locations each 500m intervals were selected for the present study. The proposed ANN model has been used to predict the equivalent continuous sound level (Leq) in dB (A). The model input parameters are the characteristics of the vehicular traffic flows (traffic volume, percentage of heavy vehicles and traffic speed). A comparison between the field measurement and the predicted Leq from neural network approach and the regression analysis was done. The results show how the neural network approach provides better performance than the classical solution based on statistical analysis.