The world's parasitic and mutualistic biodiversity is undergoing major upheavals related to modifications in host community structures, changes in interactions between species, and through coextinction events. Trematodes are an important component of this invisible biodiversity, in terms of species richness, but also because of their role in ecosystems functioning and in the emergence of associated diseases. Together, these elements point to the need for a better assessment and understanding of the structure and dynamics of trematode diversity. In this context, our aim was to develop an optimized eDNA-based metabarcoding approach to detect trematodes and characterize their communities, most of which associated to aquatic environments. The efficiency of this newly developed tool was first assessed by exhaustive in silico and in vitro validation steps. We next assessed the ability of our eDNA-based approach to reconstruct trematode communities compared to a classical trematode monitoring method over four freshwater aquatic ecosystems. Our eDNA-based monitoring tool displayed a high amplification enrichment of trematode DNA, a 100% detection score for tracking back an in vitro mock community composed of 28 trematode species, and high genetic resolution which makes it relevant to discriminate between even phylogenetically close trematode species. Over the four natural ecosystems screened in natura, 33 OTUs were generated from the eDNA-based approach, from which 11 trematode species were identified. In comparison, we identified five trematode species using the classical monitoring method, three of which were also detected by the eDNA-based approach. We believe that this new eDNA-based metabarcoding tool will open new perspectives for fundamental and applied research in community ecology, conservation, and health survey.