The determination of heat transfer coefficient plays an important role in optimal designing of heat transfer equipments as it directly affects the heat surface area and thereby the weight and cost of the equipment. Thus, prediction of heat transfer coefficient with minimum error reduces the exhaustive experimental work. Therefore, the prime objective of the present work is the application of computational intelligence methods for improving the prediction accuracy of heat transfer coefficient in flow boiling over tube bundles. The adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) are applied to predict the flow boiling heat transfer coefficient as output, taking pressure, pitch of the bundle, heat flux, mass flux and vapour quality as input. The performance of different derivatives of neural network such as multilayer perceptron, general regression neural network and radial basis network (RBF) is studied with varying the parameter. The ANFIS is tested with different types and number of membership functions for the prediction of flow boiling heat transfer coefficient. These methods are found to be better for predicting the flow boiling heat transfer coefficient than conventional correlations.