Continuous evaluation of groundwater quality is vital for ensuring its long-term sustainability. However, traditional assessment methods for various purposes face challenges due to cost and time constraints. In this study, machine learning (ML) models, including Gaussian Process Regression (GPR), Decision Tree (DT), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were employed to predict five irrigation water quality (IWQ) indices using only physical parameters (electrical conductivity (EC) and pH) and site conditions (Elevation, depth to water table, and distance to river). A dataset of 246 groundwater samples from the Eocene aquifer in Minia, Egypt, was collected and analyzed to measure groundwater quality parameters. Five combinations of the input parameters were utilized to calculate IWQ indices: sodium adsorption ratio (SAR), sodium percentage (Na %), total hardness (TH), permeability index (PI), and Kell’s ratio (KR). ML models were developed to estimate IWQ parameters based solely on physical measurements and site conditions. The results revealed that GPR, DT, SVR, and ANN strongly predicted all IWQ parameters during training. The results demonstrated that GPR accurately predicted groundwater quality, followed by DT, SVR, and ANN. The best performance of the GPR model was achieved during the fourth combination, which includes EC and distance to the river. The evaluation of GPR through the fourth combination revealed the highest accuracy with a correlation coefficient of 0.97, 0.82, 0.96, 0.87, and 0.81 in predicting SAR, %Na, TH, PI, and KR. The study emphasizes the capacity of machine learning models to efficiently employ readily available and quantifiable field data to predict IWQ characteristics. Moreover, the research findings, contributing to the second goal of the Sustainable Development Goals (SDGs), “No Hunger,” and the sixth goal, “Clean water and sanitation,” have the potential to enhance agricultural productivity and water conservation.

Securing sufficient food has become a global challenge threatening human survival. Consequently, many governments plan agricultural reclamation projects to increase food production, primarily relying on groundwater due to limited surface freshwater. However, over-exploitation of groundwater jeopardizes the sustainability of these vital resources. This research investigates the impact of one of these agricultural projects on groundwater levels in the Minia governorate of Egypt. Also, this study investigates the effects of groundwater levels on the power needed to extract it. A calibrated 3-D MODFLOW model has been utilized to simulate the response of aquifers, respecting the existing pumping rates and the subjecting of new extension agricultural areas. The findings of the drawdowns demonstrate a decrease in groundwater levels of 21.0, 33.0, 40.0, and 42.0 m, respectively, for 2030, 2050, 2070, and 2100 respecting the current operating pumping wells. Based on the proposed new agricultural extension project, these drawdowns will increase to 24.0, 42.0, 48.0, and 51.0 m for the same investigated years. The values of drawdowns are consistent with the permissible limits of drawdowns, recommending going forward with the project. Groundwater depths range from 60 to 105 m, leading to energy requirements between 25 and 44 kWh, corresponding to an area of 11 to 19 m² of solar panels. As groundwater levels decline, energy demands will increase by 43%, 50%, 60%, and 66% by 2030, 2050, 2070, and 2100, respectively. Energy requirements will necessitate a corresponding expansion of solar panel areas by 46%, 61%, 69%, and 76% by 2030, 2050, 2070, and 2100, respectively. The study’s findings advocate for the ongoing agricultural project, aligning it with the Sustainable Development Goals (SDGs) focused on addressing desertification, poverty, and hunger. Additionally, the research emphasizes the necessity of combining scientific modelling with policy development to foster sustainable agricultural practices while protecting vital water resources.