Abstract: In recent years, accelerated industrialization and urbanization have intensified soil and groundwater contamination, posing serious threats to environmental safety and public health. Accurate and efficient source-tracking technologies are therefore critical for effective remediation and responsibility attribution. Conventional tracing approaches, such as statistical analyses, pollutant fingerprinting, and inverse transport modeling, typically rely on a single medium and a single method. Emerging techniques, including remote sensing, geophysical methods, and machine learning algorithms, have attracted growing attention, each offering distinct strengths and constraints for specific application scenarios. However, the complexity of pollution sources and the high heterogeneity of environmental media continue to hinder practical tracing efforts, which are often constrained by limited data resolution, high costs, and challenges in method integration. To address these challenges, this study proposes a high-precision, multi-technology integration framework for tracing soil and groundwater pollution sources. By reinforcing multi-source data fusion, the framework simultaneously lowers costs and enhances the precision of source apportionment, thereby improving the applicability and smart capabilities of source-tracking technologies and providing robust technical support for soil and groundwater pollution prevention and control.