It is proposed that transcriptomic mechanisms may be associated with suboptimal immune responses and disease in early postpartum dairy cattle. The influence of transcriptomic changes has yet to be defined, but an investigation into differential gene expression offers an opportunity to illustrate their potential impact. The aim of this study was to utilize RNA-sequencing data to demonstrate specific transcriptomic changes within peripheral immune cells during the early postpartum period in dairy cows with and without uterine infection along with associated physiologic changes. This study was conducted on a high-producing conventional dairy with 5,000 Holstein cows. Cows in the first 14 DIM were assessed for evidence of abnormal uterine discharge. Eleven cows were enrolled as diseased cases with postpartum physiologic dysfunction based on the presence of fetid uterine discharge along with hematologic derangements indicative of a response to an infectious or altered metabolic state. Nine healthy matched cohorts were enrolled based on comparable DIM, lactation number, and no evidence of uterine or systemic infection. Blood was collected on the day of enrollment and then weekly for a total of 3 samples per cow from which RNA was extracted for downstream sequencing in all 3 samples per cow. The transcriptomic impact of early postpartum physiologic dysfunction was evaluated using feature selection with the machine learning platforms Boruta-Python and SciKit-Learn. Initial results show a set of approximately 20 genes whose expression was found to be associated with disease in post-parturient dairy cattle (Boruta p-value < 0.05; outperforms 100% of shadow features). Using functional enrichment analysis, based on gene ontology annotations (Fisher’s p-value < 0.01), these genes are related to biological processes involved in immune cell and receptor function, tissue repair, and cell signaling. These findings provide promising candidate genes for describing uterine health and identifying postpartum dysfunction. Improved understanding of transcriptomic disease mechanisms may lead to targeted interventions and allow producers a more refined means of providing optimal care.