In this work, two neural networks namely Cascade Feed Forward Neural Networks (CFFNN) and Feed Forward Neural Networks (FFNN) are modelled for the prediction of shell side pressure drop and heat transfer coefficient heat exchanger from its hot water flow rate. Simulation results showed that for same shell side mass flow rate, Heat transfer coefficient, pressure drop and heat transfer rate are found to be maximum with single segmental baffles. All the two ANN models are trained, validated and tested which predicted the shell side pressure drop and heat transfer coefficient of the Heat exchanger with acceptable accuracy and the both Neural Network is found to have the best accuracy by having the best Regression due to its feedback connections