This paper describes a robust support vector regression (SVR) methodology that offers superior performance for important process engineering problems. The method incorporates hybrid support vector regression and differential evolution technique (SVR-DE) for efficient tuning of SVR meta parameters. The algorithm has been applied for prediction of pressure drop of solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed SVR correlation noticeably improved prediction of pressure drop over a wide range of operating conditions, physical properties, and pipe diameters.