<p>Karst aquifers are critical freshwater resources developed in soluble rocks such as limestone and dolomite, characterized by complex hydrological features including conduits, fractures, and sinkholes. Over the past decade, numerous modeling approaches have been applied to investigate different aspects of karst aquifer behavior. This review synthesizes recent advances in modeling techniques used to describe karstification processes, recharge, groundwater flow, storage, and discharge, while highlighting the challenges posed by heterogeneous porosity and fracture–conduit systems. A wide range of modeling approaches is reviewed, including conceptual, laboratory-scale, analytical, numerical, and data-driven models. These approaches aim to represent preferential flow paths, groundwater movement, and contaminant transport under varying hydrological conditions. Numerical studies employing both functional and physically based models are critically evaluated in terms of their ability to simulate karst hydrodynamics using geological, hydraulic, and hydrochemical parameters. Model strengths and limitations are discussed with respect to data availability, spatial scale, and dominant flow processes. Recent developments in machine learning and advanced data analytics are reviewed as complementary tools rather than standalone solutions, with particular emphasis on hybrid and theory-guided modeling frameworks. These approaches show promise in improving predictive capability while preserving physical interpretability. This review highlights current challenges and emerging opportunities in karst aquifer modeling and provides guidance for selecting appropriate modeling strategies to support groundwater management, contamination risk assessment, and sustainable use of karst water resources.</p>