Mixed infection with multiple species of nontuberculous mycobacteria (NTM) is difficult to identify and to treat. Current conventional molecular-based methods for identifying mixed infections are limited due to low specificity. Here, we evaluated the utility of whole-genome sequencing (WGS) analysis to detect and identify mixed NTM infections. Analytical tools used included PubMLST, MetaPhlAn3, Kraken2, Mykrobe-Predictor and analysis of heterozygous SNP frequencies. The ability of each to identify mixed infections of NTM species was compared. Sensitivity was tested using 101 samples (sequence sets) including 100 in-silico simulated mixed samples with various proportions of known NTM species and one sample of known mixed NTM species from a public database. Single-species NTM control samples (155 WGS samples from public databases and 15 samples from simulated reads) were tested for specificity. Kraken2 exhibited 100% sensitivity and 98.23% specificity for detection and identification of mixed NTM species with accurate estimation of relative abundance of each species in the mixture. PubMLST (99% and 96.47%) and MetaPhlAn3 (95.04% and 83.52%) had slightly lower sensitivity and specificity. Mykrobe-Predictor had the lowest sensitivity (57.42%). Analysis of read frequencies supporting single nucleotide polymorphisms (SNPs) could not detect mixed NTM samples. Clinical NTM samples (n = 16), suspected on the basis of a 16S–23S rRNA gene sequence-based line-probe assay (LPA) to contain more than one NTM species, were investigated using WGS-analysis tools. This identified only a small proportion (37.5%, 6/16 samples) of the samples as mixed infections and exhibited only partial agreement with LPA results. LPAs seem to be inadequate for detecting mixed NTM species infection. This study demonstrated that WGS-analysis tools can be used for diagnosis of mixed infections with different species of NTM.