Abstract: Public health laboratory testing involves a wide range of sample types, complex matrices, diverse target analytes with varying concentrations, and multiple application contexts with different analytical requirements. As a critical step in public health laboratory analysis and testing, sample pretreatment plays a decisive role in ensuring the reproducibility and efficiency of the analytical methods. It directly affects the accuracy, sensitivity, and reliability of testing results, as well as the feasibility of downstream analyses. Traditional sample pretreatment techniques face persistent challenges, including low efficiency, limited throughput, restricted universal applicability, high organic solvent consumption, and poor compatibility with downstream analytical procedures. These limitations constrain their capacity to meet the evolving demands of research and practice in public health and preventive medicine. In recent years, technological advances have focused on improving efficiency and automation, enhancing selectivity and sensitivity, facilitating online testing capabilities, and promoting environmental sustainability. Sample pretreatment techniques in public health laboratory testing have been undergoing progressive upgrades, and numerous novel technologies have emerged. The paper provides a comprehensive review of new technologies and applications in the field. We focused on the development of new materials, the application of artificial intelligence, connections for online processing, and the approaches tailored to the demands of specific testing settings. We also discussed sample processing for omics analyses and mass spectrometry imaging methods relevant to public health laboratory testing. These advances are expected to support the development of greener and higher-throughput sample pretreatment and foster innovation in the public health laboratory testing system.