Groundwater is an essential resource that meets all of humanity’s daily water demands, supports industrial development, influences agricultural output, and maintains ecological equilibrium. The objective behind this research is to find groundwater prospective zones and their sustainable development in Henan Province, located in the North China Plain, with the help of analytical hierarchical process (AHP) and frequency ratio (FR) models. Consequently, a groundwater inventory map was created, utilizing 70% of the data for training and 30% for testing purposes. The resulting maps were overlaid with groundwater levels and the locations of wells within the study area. The performance and validation of the models were assessed using the Area Under Curve (AUC) curve, which indicated an accuracy of 70% for the AHP model and 69% for the FR model. The study indicates that areas with suitable gradients and rainfall patterns generally have high groundwater prospective zonation (GWPZ), i.e., Zhumadian and Nanyang (ZN). However, AHP demonstrated a higher predictive capability in comparison to FR the broader context. Whereas GIS-RS methodology aligns with existing wells and field data that validate it in range of good and excellent. In conclusion, this study provides valuable insights for effective groundwater exploration and sustainable water supply planning in Henan Province.

Monitoring GWL over extended periods is crucial for comprehending the fluctuations of groundwater resources in the present context for ongoing global changes. This study analyzed the effects of climate variations on the GWL in Henan Province North China Plain using two deep-learning models Bidirectional Long Short-Term Memory (BidLSTM) and Gated Recurrent Unit (GRU). These models predicted monthly variations in GWL at 85 monitoring wells across the area using a dataset from 1980 to 2015. For validation and evaluation, both models were quantitatively calibrated using training set (1980–2015) to predict GWL from 2016 to 2100. The dataset was partitioned, with 80% allocated for training and 20% for testing. The result interpreted that in AHP3 well, GWL declined to 120 m in 1980 due to reduced precipitation 57 mm and Et 62 mm, while temperature stayed at 10 °C as of 2070, In the Zhengzhou and Keifing regions GWL declined by 98 m in the 1980 s despite rising precipitation 72 mm and Et 60 mm, due to insufficient recharge by 2100, GWL is expected to reach 140 m, driven by climate changes, including a temperature increase to 17 °C. The results indicated significant changes with the effect of precipitation, significant increase in temperature and surface Et. Anthropogenic activity also impacted GWL in the area. The trained models demonstrated good performance, with a prediction error of 0.0350, 0.0346 m, and the root mean square error (RMSE) was recorded at 0.1870, 0.1860 m. By accurately predicting GWLs, the BidLSTM model can help ensure that groundwater resources are used sustainably and efficiently.