The paper introduces the Taxon-Independent Community Index (TICI), a novel method for assessing ecological health using Environmental DNA (eDNA) metabarcoding. TICI assigns indicator values to amplicon sequence variants (ASVs) to provide a single, interpretable metric of ecological condition, improving the statistical power and utility of eDNA-based bioassessments. The TICI model is trained using the Chessman iterative learning algorithm, which assigns health indicator scores to a large number of ASVs commonly found across various geographic ranges. New sites can be evaluated by averaging the indicator values of the ASVs present. The model was trained on eDNA data from 53 well-studied riverine monitoring sites in New Zealand, with each site sampled 16 times using eight short-amplicon metabarcoding assays. The TICI scores were strongly correlated with historical stream condition scores from macroinvertebrate assessments (R² = 0.82) and showed minimal variation between sample replicates (CV = 0.013). This demonstrates the potential of taxon-independent eDNA analysis for ecological health assessment, offering a precise and repeatable measure that can be extended to other environments. The TICI provides a scalable and accessible tool for environmental management, addressing the limitations of traditional morphology-based methods and eDNA data interpretation.The paper introduces the Taxon-Independent Community Index (TICI), a novel method for assessing ecological health using Environmental DNA (eDNA) metabarcoding. TICI assigns indicator values to amplicon sequence variants (ASVs) to provide a single, interpretable metric of ecological condition, improving the statistical power and utility of eDNA-based bioassessments. The TICI model is trained using the Chessman iterative learning algorithm, which assigns health indicator scores to a large number of ASVs commonly found across various geographic ranges. New sites can be evaluated by averaging the indicator values of the ASVs present. The model was trained on eDNA data from 53 well-studied riverine monitoring sites in New Zealand, with each site sampled 16 times using eight short-amplicon metabarcoding assays. The TICI scores were strongly correlated with historical stream condition scores from macroinvertebrate assessments (R² = 0.82) and showed minimal variation between sample replicates (CV = 0.013). This demonstrates the potential of taxon-independent eDNA analysis for ecological health assessment, offering a precise and repeatable measure that can be extended to other environments. The TICI provides a scalable and accessible tool for environmental management, addressing the limitations of traditional morphology-based methods and eDNA data interpretation.