Precise seasonal prediction of groundwater nitrate concentrations in intensive agricultural areas faces challenges such as data sparsity, strong spatiotemporal heterogeneity, and complex hydro-biogeochemical processes. To address these issues, this study proposes an integrated prediction framework combining hybrid quantum-classical machine learning, advanced virtual sample generation (t-SNE-GMM-KNN), and remote sensing foundation model semantic embedding (AEF). Modeling was conducted across the 2022<span>–</span>2023 normal, dry, and wet seasons in Xiong'an New Area. Hydrochemical types were dominated by Ca-Mg-HCO<sub>3</sub><span>−</span>, controlled by mineral dissolution and evaporation. Nitrate concentrations were highest in the dry season (mean 42.93 mg L<sup><span>−</span>1</sup>), driven by evaporative concentration. Spatially, high-value zones shifted: southeast (normal), central (dry), and northwest (wet). MixSIAR modeling based on isotopes indicated domestic sewage and livestock manure (74.1 %) as dominant sources. The t-SNE-GMM-KNN strategy mitigated small-sample bias while preserving nonlinear structure. When virtual samples were augmented to 10-fold, the Random Forest R<sup>2</sup> in the dry season increased from 0.284 to > 0.85. Furthermore, a hybrid quantum-classical Random Forest exhibited superior robustness for data sparsity, achieving peak performance in the normal season (R<sup>2 </sup>= 0.962, RMSE = 5.73 mg L<sup><span>−</span>1</sup>). Additionally, using only AEF embeddings achieved screening-level accuracy (R<sup>2</sup> up to 0.860), providing a feasible rapid survey scheme for extensive unmonitored regions. Correlation analysis identified TDS and EC as persistent top predictors (r > 0.8). This comprehensive framework offers a robust solution for seasonal nitrate prediction and sustainable water management.