The current study presents a new methodology for Gasketed Plate Heat Exchanger (GPHE) efficiency prediction using as a basis a Computational Fluid Dynamics (CFD) model, detailed enough to reproduce the complicated characteristics of the induced flow patterns exhibited in a single flow channel characterized by difficult geometrical patterns. Compared to other similar approaches an iterative procedure for the imposition of the most appropriate thermal boundary conditions is proposed. For the presentation of this methodology and its validation against available design data, a model of an existing GPHE consisting of 24 plates (thus 23 channels) was tested. At first a mesh independence study was performed, while with the selected grid density a deviation of around 15% of numerical predictions for the calculated overall pressure drop compared to the design values was tracked. This level of discrepancy can be attributed to the uncertainty of channel spacing, which influences significantly the pressure losses and for which unfortunately no concrete measurements can be made, owed to no actual access to the plate manufacturing process itself. With the use of the CFD model, the strong dependency between pressure drop and plate distance was quantified, as for an increment of 1.88% of the plate pack length, a 39% decrease in pressure drop was calculated. This highpoints the importance of plate tightening process, after its manufacturing, as a crucial specification parameter during the design of such type of heat exchangers; highlighting at the same time how the proposed CFD tool can act as an important supporting tool in the design of such type of complicated Heat Exchangers. Following the prediction of pressure drop, a new proposed boundary conditions imposition approach was applied in the model, making it possible to estimate the whole GPHE heat transfer efficiency with a 6% error compared to the design values The developed and validated methodology can facilitate especially the design of large scale GPHE, since long-term testing campaigns for the optimization of such components, especially in industrial applications, are highly costly.