Electrospinning is the process of extruding a fine fiber from a charged polymer solution. The fiber is continuously stretched by electrostatic forces and the evaporation of solvent while traveling through air prior to deposition. These fibers provide a high surface area to volume ratio and a high porosity which are useful in many engineering areas such as biomedical materials engineering. But one major issue with the process is the lack of a functional model that can link processing parameters and polymer solution properties to fiber morphology (fiber diameter and its distribution). Most developed models so far are only functional for a specific polymer-solvent System. In this study, we present the application of the gene expression programming (GEP) model for the prediction of the electrospun fiber morphology from polymer solution properties. Poly(ethylene oxide) PEO)/poly(ethylene glycol) (PEG) solution characteristics such as surface tension (mN/M), conductivity (mu S/cm), viscosity (Pa s), relaxation time (s), FED molecular weight (g/mol), elastic modulus (Pa) and extensional stress (Pa) are used as predictor variables. The prediction accuracy is compared to that of classification and regression trees (CART) method. The results show that the GEP model predicts better than the CART model and is promising for nanofiber morphology prediction.