<p>Groundwater is a critical freshwater resource that underpins India’s agricultural productivity and drinking water security, yet it faces increasing stress from anthropogenic pressures, particularly coal mining. This study assessed mining-induced groundwater level (GWL) decline in the Moher sub-basin of the Singrauli coalfield using ground-based and satellite observations from 2006/2007 to 2020. Satellite analysis revealed a substantial expansion of mining activities, with the mining area increasing from 34.77 km² in 2007 to 92.47 km² in 2020. The Mann–Kendall test and Sen’s slope estimator indicated a statistically significant increasing trend (Z = + 5.15) with an average annual growth rate in mining area of 4.22 km²&#xa0;year⁻¹. Normalized difference vegetation index (NDVI) analysis showed a marginal rise in mean vegetation index (0.200 to 0.242), suggesting limited ecological recovery, while persistently low NDVI values over central mining zones indicate continued landscape disturbance. Despite an increasing rainfall trend, GWL exhibited consistent declines near active mines, with depletion rates of 0.63&#xa0;m&#xa0;year⁻¹ (pre-monsoon) and 0.59&#xa0;m&#xa0;year⁻¹ (post-monsoon). The dynamic groundwater reserve declined by approximately 5.1 million m³ within a 77.65 km² zone of influence, primarily due to mine dewatering. To simulate and predict GWL variation, an artificial neural network (ANN) model was developed using mining area, monsoon rainfall, temperature, and evaporation as predictors. The two-layer feed-forward and nonlinear auto regressive exogenous (NARX) networks demonstrated strong predictive performance, emphasizing the utility of machine learning in quantifying hydrological impacts of opencast mining and supporting sustainable groundwater management and environmental impact assessment (EIA).</p>