Abstract: Groundwater pollution, characterized by its concealment, delayed manifestation, and high heterogeneity, makes accurate source identification a critical foundation for effective pollution prevention, control, and remediation strategies. Traditional single-method tracing techniques—such as pollutant fingerprint analysis, mathematical statistics, numerical simulation, and deep learning—have long been constrained by a persistent tension among accuracy, efficiency, and mechanistic interpretability. This limitation hinders their ability to address emerging challenges including complex media structures, multi-source contamination, and novel pollutants. To overcome these bottlenecks, the integration of multiple methods has become an essential trend in groundwater pollution tracing research. This paper systematically reviews the three-stage evolution of the field—from initial simple combination to algorithmic coupling, and further towards deep interdisciplinary integration. It focuses on core challenges such as ambiguous contaminant migration pathways in heterogeneous media, overlapping contributions from multiple pollution sources, and trace-level detection of emerging contaminants. The study analyzes integration pathways and evaluates application outcomes across key scenarios, such as industrial sites, agricultural non-point sources, fractured aquifers, drinking water source areas, and cases involving emerging contaminant. Furthermore, the study highlights persistent challenges related to the compatibility, interpretability, cost-effectiveness, and generalizability of current integrated technologies. Future directions are proposed, including the development of intelligent tracing platforms, deeper integrating mechanistic models with artificial intelligence, establishment of standardized frameworks, and promotion of cost-effective technological solutions. These advances are anticipated to drive a paradigm shift in groundwater pollution management from passive response toward precision prevention and control.