In forensic science, genotyping mixed DNA is a critical and complex task. Sequencing errors and allele sharing complicate the analysis, particularly in cases involving unbalanced mixtures, multiple contributors, and kinship relationships. Massively parallel sequencing (MPS) panels comprising highly polymorphic microhaplotypes (MHs) offer a promising approach for detecting unique alleles in mixtures with a mixture ratio greater than 10:1, involving more than two contributors or contributors with kinship. However, sequencing errors such as base substitution and InDels on the MPS platform remain a significant challenge in genotyping complex mixed DNA. The barcoding approach has been introduced to MPS to distinguish true alleles from sequencing errors. This method employs unique molecular identifiers (UMIs) to tag individual DNA molecules, allowing for the identification and correction of random sequencing errors. By generating consensus sequences from read replicates associated with the same UMI, this approach enhances the accuracy of allele detection. In this study, UMIs were incorporated into developing a highly polymorphic panel consisting of 105 MHs, with an average effective number of alleles (Ae) of 6.9. Various types of mixed DNA samples were prepared, including unbalanced mixtures with ratios ranging from 1:1-160:1, multi-contributor mixtures with 2-6 contributors, and kinship-involved mixtures with parent-offspring to fourth-degree relatives contributors. Unique alleles were quantified, and mixture proportions (Mx) were calculated separately using sequencing reads and the number of UMI families with more than 10 members. The results demonstrated that UMI played a critical role in identifying sequencing errors and enhancing the accuracy of allele genotyping in unbalanced mixtures. A strong correlation (R² = 0.96) between UMI count and DNA template amount demonstrated that DNA template amount could be inferred from UMI count. Mx values derived from the number of UMIs were consistent across loci and showed a high correlation with mixture ratios (R