All fundamental aspects of human physiology, metabolism, and behavior display 24-hour rhythms, and at least 40% of protein-coding genes show daily rhythms in expression in a tissue-specific manner in humans. Our ability to incorporate circadian timing into clinical decision-making is impaired by our inability to measure circadian time quickly and easily, impacting not only treatment for patients with suspected circadian rhythm disorders and other sleep pathologies, but limiting our ability to pursue chronomedicine (delivering treatments at the optimal time of day so as to increase efficacy and/or reduce side effects) as factor in personalized medicine. We used a high throughput plasma proteomics approach combined with machine learning to accurately predict internal circadian time (within 1hr). Our results show viability to fill this technological gap by establishing and refining a plasma proteomic circadian time predictor that can estimate circadian time in an individual with a single blood sample taken at any time during the day or night.