Groundwater serves as a lifeline in Egypt's hyper-arid Eastern Desert, particularly for agricultural and domestic uses. However, a comprehensive understanding of groundwater origin, quality, and recharge dynamics in the region remains limited due to geological complexity, data scarcity, and the high cost of isotopic analysis. This study addresses these challenges by integrating stable isotopes (delta O-1(8) and delta H-2), hydrogeochemical parameters, remote sensing, and explainable artificial intelligence (AI) to investigate groundwater dynamics and support sustainable water management strategies. A total of 34 groundwater samples were collected from three key aquifers: the Quaternary alluvium, Nubian Sandstone, and fractured Basement aquifers. Hydrochemical analyses and isotopic signatures distinguish meteoric water from paleowater sources, revealing significant mixing and recharge processes. The findings indicate that the Quaternary aquifer is increasingly influenced by upward leakage from the Nubian aquifer, facilitated by deep-seated faults. Between 2014 and 2021, water levels in the Quaternary aquifer declined by up to 14 m due to over-extraction, particularly in agricultural zones. To enhance predictive capabilities, a Support Vector Machine (SVM) model was developed to estimate delta O-1(8) values using multiple hydrochemical indicators, achieving strong performance (R-2 = 0.92, MSE = 2.89). SHapley Additive exPlanations (SHAP) analysis identified Mg-2(+), HCO3-, and SO42- as dominant factors influencing isotope variation. This integrated approach represents a novel application of explainable machine learning in hydrogeology and offers a scalable, cost-effective tool for assessing groundwater systems in arid regions. The study contributes directly to national water security goals and supports the global Sustainable Development Goal 6 (SDG 6) for clean water and sanitation. The graphical abstract summarizes the interdisciplinary methodology and key findings of the study through a sequence of integrated visual components. At the center, the schematic flowchart divides the study into two main components: determining the origin of groundwater and identifying sustainable development zones. The top-left isotopic plot (delta H-2 vs delta O-1(8)) differentiates water sources such as Nubian, Quaternary, Nile, and rainwater, highlighting mixing lines and recharge pathways. The top-right panel displays NDVI change maps from 2014 and 2021 derived from remote sensing, indicating vegetation dynamics and land-use changes. The adjacent geological cross-section illustrates aquifer systems, stratigraphy, and groundwater level decline over time. The bottom-left hydrogeological conceptual model visualizes groundwater flow paths, recharge zones, fault systems, and upward leakage from the Nubian aquifer into the Quaternary aquifer. The center-bottom panel presents SHAP output plots from the explainable machine learning model applied to predict oxygen isotopes, identifying the most influential hydrochemical variables. The bottom-right map delineates groundwater-based sustainable development zones (poor, moderate, good) using a composite analysis of hydrochemical, geological, and remote sensing data. Together, these visual elements effectively communicate the integrated methodology and key findings of the study, highlighting the innovative use of isotopic analysis, machine learning modeling, and geospatial tools for groundwater assessment and sustainable development planning in Egypt's Eastern Desert.