In the present study, the parameter responsible to find out pressure drops in a pipeline network system has been modeled by Gene Expression Programming Based on the experimental data. The different factors like Pipe diameter, Particle diameter, liquid density, Solid density liquid Viscosity, Volume fraction, Velocity, Solid concentration are taken into consideration as the input parameter. GEP model was developed to predict the pressure drop within the pipeline system. GEP model predicts the pressure drop with an accuracy of mean R-Square 0.999153373.As the input parameter is responsible for the selection of soft computing method and both ANN and GEP model is considered in order to validate the output parameters. The result of GEP has been compared with an ANN model, to observe the level of accuracy of the predicted pressure drop with a correlation to predict pressure drop shown by equation 6. The obtained results of both GEP and ANN models are being compared and GEP predicted results are found to be better in predicting the output parameter. The mean absolute error is found to be 15.566 % by the ANN model wherein the GEP model predicts with an accuracy of 8.993 %.The results indicate that the GEP is better tool to predict pressure drop with more accuracy.