The endogenous microbiome of healthy individuals in oral cavities is diverse, representing over 700 bacterial species. Imbalance in oral and gut microbiome composition and associated gene expression has been linked to different forms of hematological (blood) cancers. Our objective is to compare oral microbiome profiles of patients with blood cancers (BC group: N = 39 patients, n = 124 oral samples) to those of healthy control subjects (HC group: N = 27 subjects, n = 100 oral samples). Saliva samples and swabs of buccal mucosa, supragingival plaque, and tongue were collected from blood cancer patients and healthy controls. Next-generation sequencing (16S-rRNA gene V3-V4 region) was used to determine the relative abundance of bacterial taxa present at the genus and species levels. Differences in oral microbiome -diversity were determined using multivariate permutational analysis of variance (PERMANOVA). Linear discriminant analysis (LDA) effect size (LEfSe) analysis was performed to identify differentiating bacterial taxa in pairwise comparisons. The PATRIC online tool was used to determine the predominance of potential pathogenicity in the BC group. The oral microbiome -diversities of the BC and HC groups differed and corresponded to a reduced -diversity in the BC group. LEfSe analysis showed significant LDA scores for and spp., differentiating the BC group from the HC group. In silico analysis using PATRIC demonstrated that the groups of bacteria possessing traits of "antibiotic resistance", "oral pathogen", and "virulence" was enriched in the BC group. Although 56% of the BC patients received antibiotics within two weeks of the oral bacterial sampling, remained the top differentiating feature in the BC group regardless of the administration of antibiotics, while was detected as the top differentiating feature in the BC patients who did not receive antibiotics, but not in those who received antibiotics. Further investigation is needed to better understand the interactions of certain oral species with the host immune system to better characterize clinically relevant associations with hematological cancers.