In hydrogeology, assessing key aquifer hydraulic parameters such as transmissivity (T), hydraulic conductivity (K), and porosity (PHIE) is crucial for effective groundwater management. Traditionally, these parameters are obtained through pumping tests and well log data. However, porosity logs are often lacking in most groundwater wells. While neutron density logs are commonly used for porosity estimation, our study uniquely employs resistivity logs to calculate porosity due to the scarcity of recorded logs in groundwater exploration. Consequently, this research aims to use conventional well log and hydrogeological data to predict T, K, and PHIE using machine learning (ML) algorithms, including random forest (RF), gradient boosting (GB), linear regression (LR), and support vector machines (SVM). This methodology is applied as a case study in the Nubian Sandstone Aquifer (NSA) in Farafra Oasis, Egypt. Firstly, T and k values were determined by analysis of the long duration pumping test records for ten wells penetrated the NSA. The performance of the ML algorithms in predicting transmissivity and hydraulic conductivity was rigorously evaluated using test wells. The RF model demonstrated superior accuracy, with predicted values of T and K being 113.11 m2/h and 0.2271 m/h in well W-6, and 104.15 m2/h and 0.1867 m/h in well W-8, respectively. The close agreement among actual and predicted values underscores the RF model's reliability in estimating these parameters, effectively identifying the fundamental trends within the dataset. For porosity prediction, the RF and GB models exhibited excellent correlation with log-derived PHIE, achieving correlation coefficients of 0.95 and 0.96, respectively. In contrast, the LR model showed acceptable performance, while the SVM model had comparatively lower correlation. These findings highlight the potential of ML models, particularly RF and GB, in accurately predicting key aquifer hydraulic parameters, thereby enhancing the understanding and management of the groundwater aquifers.