With the shrinking of technological nodes, analysis of nanosized-metal-grain pattern-dependent devices is becoming critical; various machine learning (ML) approaches have been utilized to study device characteristic and variability. The inevitable dataset, one of the requisite ML techniques, can be overcome by considering the transfer learning (TL) approach. In this work, an analysis of electrical characteristic affected by work function fluctuation (WKF) with a limited amount of dataset of gate-all-around (GAA) silicon (Si) nanofin (NF) field-effect transistors (FETs) is advanced along with the combination of collected data of GAA Si nanosheet (NS) FETs and TL models. Comparison of the baseline ML model and the proposed TL model shows significant improvement in terms of the values of root mean square error (RMSE) and R2-score. One of applications of this work is to estimate the WKF-induced variability without executing a huge amount of three-dimensional device simulation of GAA Si NF FETs.