Abstract: Acid mine drainage (AMD) is a typical form of complex pollution generated during mineral resource development. Its low pH, high concentrations of metal ions, and sulfate load exert pervasive cross-media stress on regional aquatic environments, soil ecosystems, and biological health. To systematically analyze the research progress on AMD ecological risk assessment, this study integrates cutting-edge findings and representative case studies. Based on a clear understanding of the causes of pollution, it explicitly delineates risk sources, driving factors, exposure pathways, and multi-tiered ecological endpoints, and reviews the characteristics and limitations of current mainstream assessment systems. The results indicate that clearly defining the assessment targets, scales, and methods is fundamental to revealing the mechanisms of risk formation and achieving effective control. AMD risk characteristics are significantly regulated by different combinations of driving factors, with the contribution of combined natural and anthropogenic risk sources being particularly notable. Existing assessment systems mainly include environmental benchmark assessment, exposure-effect assessment, and multi-scale integrated assessment, and their methodologies are undergoing a paradigm shift from static indices and bio-toxicity tests to process mechanisms and dynamic model evaluations. However, current assessment practices are still hampered by a suite of interconnected uncertainties, including limited data representativeness, overly complex model parameterization, and insufficiently robust risk characterization. To address these limitations, future research can synergistically integrate multi-source remote sensing, isotope tracing, machine learning, and other emerging technologies to develop intelligent assessment models with dynamic early-warning and cross-scale analytical capabilities. Simultaneously, efforts should be made to promote a coordinated governance pathway of pollution control–ecological restoration–carbon sequestration enhancement to improve the accuracy of AMD risk warnings, enhance proactive management, and strengthen the sustainability of regional ecosystem services.