Monitoring pesticides in the environment is difficult due to the growing number of new pesticides used globally each year. To identify emerging pesticides for regional monitoring of waterways, a new approach is proposed. It involves identifying priority pesticides for screening based on local pesticide registration data, high toxicity to non-target aquatic organisms, and those not typically detected in routine laboratory screening. To detect pesticides in surface waters, a semi-targeted analytical approach was developed that combines two passive sampling methods with high-resolution mass spectrometry (HRMS). This study looked at the presence of 181 priority pesticides at 32 waterway sites within greater Melbourne consisting of a broad range of catchment land uses. For pesticide detection, liquid chromatography combined with quadrupole-time-of-flight mass spectrometry (LC-QTOF-MS) was used in a data-independent acquisition mode (DIA). Using a semi-targeted workflow via the Waters UNIFI software, we tentatively detected 21 out of 181 pesticides across 22 sampling sites. We were able to confirm the presence of 5 pesticides using analytical standards. We have confirmed the presence of several newly emerging pesticides in surface waters, which to our knowledge, have not previously been detected in waterways across Australia. Confirming pesticides prior to routine screening is crucial for efficiency and cost-effectiveness. However, confirming the presence of many compounds without specific data can be challenging due to practical and analytical limitations. As a result, performing comprehensive screening for both suspected and non-target pesticides can be difficult. In this study, we demonstrated that combining a regional screening approach with broad-field sampling in tandem with a semi-target HRMS is an effective way to update pesticide monitoring in regional areas. This approach helps to better understand levels of emerging pesticides in surface waters and the prioritisation of pesticides in routine screening programs.