Stony coral tissue loss disease (SCTLD) remains a substantial threat to coral reef diversity already threatened by global climate change. Restoration efforts and effective treatment of SCTLD requires an in-depth understanding of its pathogenesis in the coral holobiont as well as mechanisms of disease resistance. Here, we present a supervised machine learning framework to describe SCTLD progression in a major reef-building coral,Montastraea cavernosa, and its dominant algal endosymbiont,Cladocopium goreaui. Utilizing support vector machine recursive feature elimination (SVM-RFE) in conjunction with differential expression analysis, we identify a subset of biologically relevant genes that exhibit the highest classification performance across three types of coral tissues collected from a natural reef environment: apparently healthy tissue on an apparently healthy colony, apparently healthy tissue on a SCTLD-affected colony, and lesion tissue on a SCTLD-affected colony. By analyzing gene expression signatures associated with these tissue health states in both the coral host and its algal endosymbiont (family Symbiodiniaceae), we describe key processes involved in SCTLD resistance and disease progression within the coral holobiont. Our findings further support evidence that SCTLD causes dysbiosis between the coral host and its Symbiodinaiceae and additionally describes the metabolic and immune shifts that occur as the holobiont transitions from a healthy to a diseased state. This supervised machine learning framework offers a novel approach to accurately assess the health states of endangered coral species and brings us closer to developing effective solutions for disease monitoring and intervention.