Abstract: Precise tracing of groundwater pollution sources is a critical foundation for pollution prevention, control, and remediation. Traditional single-technology approaches exhibit significant limitations in pollution source localization, contribution analysis, computational and emergency response efficiency, and mechanistic interpretation of pollutant migration and transformation processes. In recent years, researchers have progressively integrated multiple technologies, including hydrochemistry and stable isotope fingerprinting, multivariate statistics and receptor modeling, process-based numerical modeling, optimization algorithms, and data-driven methods. This paper systematically reviews the development of multi-technology integration for groundwater pollution source tracing, and summarizes its evolution from data-complementary integration to model-synergistic coupling, and ultimately to interdisciplinary intelligent collaboration. It analyzes the application logic and existing bottlenecks of these three integration approaches. To address how multi-technology integration can synergistically enhance tracing accuracy, computational efficiency, and mechanistic interpretation, this study summarizes the technical suitability and application effectiveness of the three integration approaches, in combination with typical scenarios such as those involving industrial, agricultural, and fractured media settings, as well as emerging pollutants. The results indicate that multi-technology integration significantly enhances comprehensive tracing capability in complex scenarios. However, challenges remain in model compatibility, result interpretability, technical cost, and model generalization. Future research should focus on deepening the integration of mechanistic models and data-driven methods, developing efficient uncertainty quantification tools, and establishing standardized technical workflows. These advancements can facilitate the transition of groundwater pollution tracing from passive identification to proactive and precise prevention and control.