Comprehensive information on vertical variations in hydraulic conductivity along a borehole is indispensable for characterizing the complexity of the hydraulic properties of fractured bedrock aquifers or for use as input data for groundwater modeling. This study presents a practical method for addressing various engineering concerns (e.g., project budget, completion time, manpower, and direct measurement of hydraulic properties from rock core specimens) when obtaining detailed and continuous hydraulic conductivity data along a borehole. Based on in-situ hydrogeological data collected from 26 boreholes located in the Choshui River Basin of Central Taiwan, eight individual and six composite geological indices were investigated to determine their correlations with the hydraulic conductivity of fractured rocks through bivariate analysis. The correlation analysis results indicate that all composite geological indices have higher correlations with the hydraulic conductivity than individual indices do. Moreover, the greater the number of individual geological indices integrated into one composite index, the higher the correlation. Based on the correlation results, quantification models for predicting the hydraulic conductivity using the composite geological indices were developed using regression analysis techniques. The performances of various statistical models were evaluated and compared. The regression analysis results for all six predictive models show that a power law relationship exists between each composite geological index and the hydraulic conductivity, and that the coefficient of determination ranges from 0.77 to 0.88. Therefore, the newly developed models can serve as an alternative for obtaining vertical hydraulic conductivity data when the budget for onsite hydraulic test is limited. • Models for predicting the hydraulic conductivity in boreholes are developed. • Eight individual and six composite geological indices are used. • All composite indices have high correlations with hydraulic conductivity. • The developed models are proven to be reliable.