This study proposes a theoretical and mathematical model of a BigData-based information system for monitoring and managing groundwater across six hydrogeological deposits in the Zarafshan River valley, Samarkand Region, Uzbekistan. The conventional aggregated water-balance equation is first indexed to reflect differences in recharge mechanisms and then extended to a coupled system of dynamic differential equations that represents hydraulic connectivity among deposits. A Kalman filter is formulated to fuse sensor observations with model forecasts, while a Regional Water Security Index (RWSI) integrates data quality, water-balance deviation, and anomaly signals. An error-propagation model and a constrained optimization problem are further introduced to support the allocation of limited monitoring resources. The study is primarily theoretical and numerical; published field-test results for a time-of-flight (ToF) groundwater-level sensor are used only for an illustrative calibration of the measurement-error variance. The results provide a formal classification of deposits according to dominant recharge mechanisms, characterize inter-deposit dynamic coupling, and establish a theoretical criterion for prioritizing monitoring resources. The proposed framework provides a basis for subsequent empirical calibration with long-term observations.