Estimation of hydraulic parameters and their successful prediction in an arid and/or semiarid region are challenging due to various hydrogeological complexities. In this study, machine learning (ML) algorithms, namely random forest (RF), random tree (RT), support vector machine (SVM), Gaussian process (GP) and long short-term memory (LSTM)) are used and their performances are compared for the prediction of hydraulic parameters, utilizing multiple hydrogeological attributes deriving from a geographical information system (GIS) and geophysical investigations in Sindhudurg District, Maharashtra, India. Multihydrogeological attributes as input and hydraulic parameters-e.g., hydraulic conductivity (K) and transmissivity (T) derived by geoelectrical methods are used as a target for building the predictive models. To enhance the model performance, a logarithmic data transformation technique was employed and correlation analysis was conducted to hypothesize the different input configurations during ML model building. The data were divided into two components: training (80%) and testing (20%). Qualitative and quantitative performance measures were evaluated to examine the predictive power of the ML models. Based on the test result, RF is found to be the best predictive model with Pearson's correlation coefficients of similar to 0.93 and 0.83 for modeling K and T, respectively. Results also reveal that model performance mainly depends on ML architecture, data structure, data accuracy, and the amount of data used. Thus, the present study successfully facilitates the predictive modeling of hydraulic parameters in the study area and the proposed method could be further explored in other complex hydrogeological areas around the world.