This paper presents an ensemble machine-learning approach to monitor the blood volume decomposition state for early hypovolemia detection. Hypovolemia is one of the major causes of preventable deaths in trauma cases. The proposed algorithm discriminates hypovolemia from normovolemia and further classifies hypovolemia into relative and absolute hypovolemia. The algorithms for blood volume classification are analyzed by extracting 13 distinct features from multi-modal physiological signals including Photoplethysmogram (PPG), Electrocardiogram (ECG), and Seismocardiogram (SCG). We compared different Machine Learning classifiers for the multi-class classification problem. We have validated our algorithm on a publicly available dataset collected from six animals undergoing normovolemia, relative hypovolemia, and absolute hypovolemia conditions.