This study examines the effectiveness of generalised additive models (GAMs) and log-log linear models for estimating the parameters of the generalised extreme value (GEV) distribution, which are then used to estimate flood quantiles in ungauged catchments. This is known as parameter regression technique (PRT). Using data from 88 gauged catchments in New South Wales, Australia, flood quantiles corresponding to various Annual Exceedance Probabilities (AEP) were estimated, which were then used as dependent variables and several catchment characteristics were used as independent variables. GAMs were employed to capture non-linearities in flood generation processes. The study evaluates different GAM and log-log linear models, identifying the best ones based on significant predictors and various statistical metrics using a leave one out (LOO) validation approach. Results indicate that GAMs provide more accurate and reliable predictions of flood quantiles compared to the log-log linear models, demonstrating better performance in capturing observed values across different quantiles. The absolute median relative error percentage (REr%) ranges from 33% to 39% for the GAMs, and from 36% to 45% for the log-log models. GAM demonstrates better performance compared to log-log linear models for quantiles Q2, Q5, Q10, Q20, and Q50; however, their performance appears to be similar for Q100.