Hydrological systems are naturally complex with great spatial and temporal variation caused by several interacting processes. Often difficult to fully reflect this complexity and related uncertainties are conventional deterministic and stochastic hydrological models. A framework for measuring uncertainty, disorder, and information content inside hydrological processes based in thermodynamics and information theory is given by entropy. This work thoroughly surveys applications of entropy modeling in hydrology, therefore highlighting the unresolved issues and obstacles impeding its broad adoption. Additionally, it investigates the terrain of next-generation entropic hydrology, pointing out potential avenues for future development and research including Machine Learning (ML), multiscale analysis, and sophisticated data-driven techniques. Through the entropy perspective, the goal is to highlight the current state-of-art and plan a route for more strong, educational, and predictive hydrological research.