With the advent of precision medicine, biomarkers have recently come into focus as a promising tool for early cancer detection and treatment individualization. In particular, much interest has been shown in the oral microbiome as a promising potential cancer biomarker, especially for head and neck cancers. The American Cancer Society estimates that there will be nearly 50,000 new cases and roughly 10,000 deaths from oral or oropharyngeal cancer in 2017. The five-year survival rate for cancers of the oral cavity and pharynx is 66% for Caucasian individuals and 47% for African American individuals. However, when caught early while the cancer is at a local stage, the 5-year survival rates rise to 83% and 79%, respectively. The oral carcinoma cancer is typically diagnosed by an oral health care provider by visual screening. However, many of these cancers are discovered when they have already progressed to a later stage. The goal of this research is to evaluate oral microbiome based biomarkers for early oral carcinoma detection. The outcome of this research is a machine-learning based framework for microbiome-based early cancer detection. The ability to identify at risk patients using minimally invasive biomarkers will allow for more rapid treatment plan development and improved outcome. The early diagnosis and treatment of cancer is essential for increasing patient survival odds and mitigating patient suffering.