Karst aquifers are critical yet vulnerable water resources in semi-arid Mediterranean regions, where structural complexity, nonlinearity, and delayed hydrological responses pose significant modeling challenges under increasing climatic and anthropogenic pressures. This study examines the Jebel Zaghouan aquifer in northeastern Tunisia, aiming to simulate its natural discharge dynamics prior to intensive exploitation (1915-1944). Given the fragmented nature of historical datasets, meteorological inputs (rainfall, temperature, and pressure) were reconstructed using a data recovery process combining linear interpolation and statistical distribution fitting. The hyperparameters of the artificial neural network (ANN) model were optimized through a Bayesian search. Three deep learning architectures-Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)-were trained to model spring discharge. Model performance was evaluated using Kling-Gupta Efficiency (KGE '), Nash-Sutcliffe Efficiency (NSE), and R2 metrics. Hydrodynamic characterization revealed moderate variability and delayed discharge response, while isotopic analyses (delta 18O, delta 2H, 3H, 14C) confirmed a dual recharge regime from both modern and older waters. LSTM outperformed other models at the weekly scale (KGE ' = 0.62; NSE = 0.48; R2 = 0.68), effectively capturing memory effects. This study demonstrates the value of combining historical data rescue, ANN modeling, and hydrogeological insight to support sustainable groundwater management in data-scarce karst systems.