The increasing climate-driven replacement of kelp forests by turf algae highlights the need for efficient biodiversity monitoring. Traditionally, monitoring turf communities involves species identification based on morphology, which is challenging due to their reduced dimensions and highly variable morphology. Molecular methods promise to revolutionize this field, but their real-world effectiveness needs to be evaluated. Here, we evaluate the performance of DNA metabarcoding (COI and rbcL markers) and morphological identification (in situ and photoquadrat identifications) to describe intertidal turf communities along the Portuguese coast. When comparing metabarcoding with in situ and photoquadrat identification, it was found that both COI and rbcL markers detected more taxa than the other two (277 and 140 vs 28 and 34 taxa, respectively). Metabarcoding also showed greater discrimination of turf communities between shores and regions, matching our knowledge of the geographical and climatic patterns for the region. However, certain taxa that were identified by in situ and photoquadrat approaches were not detected through metabarcoding, likely due to lack of reference barcodes or taxonomic resolution. Our multi-marker metabarcoding approach was more efficient than morphology-based methods in characterizing turf communities along the Portuguese coast, differentiating morphologically similar species, and detecting unicellular organisms. Additionally, although not the primary focus, the COI marker identified metazoans, which can be used in future ecological studies on species co-occurrence and algae-animal interactions. Metabarcoding emerges as a valuable tool for monitoring these communities, particularly in long-term programs requiring accuracy, speed, and reproducibility.