For fluid resuscitation of critically ill to be effective, it must be well calibrated in terms of timing and dosages of treatments. Both under-resuscitation due to delayed or inadequate treatment and over-resuscitation can lead to unfavorable patient outcomes. In current practice, sufficiency of resuscitation is determined using primarily invasively measured vital signs, including Arterial Pressure and SvO2. These measurements may not be available in non-acute care settings and outside of hospitals, in particular in the field when treating subjects injured in traffic accidents or wounded in combat, where only non-invasive monitoring is available to drive care. We propose a Machine Learning (ML) approach to estimate the sufficiency of fluid resuscitation utilizing only non-invasively measured vital signs. We also aim at addressing another challenge known from literature: the impact of inter-patient diversity on the ability of ML models to generalize well to previously unseen subjects. The reference to a stable personal baseline, though an effective remedy for the inter-patient diversity, is usually not available for e.g. trauma patients rushed in for care and presenting in already acute states. We propose a novel framework to address those challenges. It uses only non-invasively measured vital signs to predict sufficiency of resuscitation, and compensates for the lack of personal baselines by leveraging reference data collected from previous patients. Through comprehensive evaluation on the physiological data collected in laboratory animal experiments, we demonstrate that the proposed approach can achieve competitive performance on new patients using only non-invasive measurements without access to their personal baselines. These characteristics enable effective monitoring of fluid resuscitation in real-world acute settings with limited monitoring resources, and can help facilitate broader adoption of ML in this important subfield of healthcare.