Extensive burn injury is not only devastating but also a significant challenge for healthcare providers. Following a chain of inflammatory responses post-burn, significant amounts of plasma shift from the vascular compartment into the tissues, simultaneously posing the risks of hypovolemic shock and edema. Standard burn resuscitation protocols aim to replace the lost blood volume while not exacerbating the edema through hourly-titrated intravenous fluid infusion. Due to the significant variability in treatment efficacy, there is a substantial ongoing effort to optimize and individualize the burn resuscitation protocols. In this work, we aim to contribute to this effort by (i) developing a platform for the virtual evaluation of burn resuscitation protocols and (ii) identifying biomarkers to guide fluid resuscitation effectively. The first part of this work presents a mathematical model of burn injury and resuscitation, which can be used for the development and non-clinical testing of burn resuscitation protocols and algorithms, as well as to garner knowledge and intuition into this complex pathophysiology. Our mathematical model consists of a multi-compartmental model of blood volume kinetics, a hybrid mechanistic-phenomenological model of kidney function, and novel lumped-parameter models of burn-induced perturbations in volume kinetics and renal function. We examined our mathematical model’s prediction accuracy and reliability using a rich dataset from 16 sheep with extensive burn injuries and clinical data from 233 real-world burn patients. The second part of this work presents the expansion of the mathematical model to incorporate the cardiovascular and renin-angiotensin-aldosterone systems, as well as detailed descriptions of the kidney’s mechanisms, particularly regarding its blood volume and blood pressure regulation roles. This expansion was motivated by the importance of cardiovascular monitoring in the critical care of burn injury patients. We trained and validated the expanded mathematical model for three species: nine sheep subjects and 15 swine subjects with rich cardiovascular and volume kinetics data, and 233 human subjects with demographic and urinary output (UO) data. To the best of our knowledge, our mathematical model may be the first of its kind which is extensively validated for use as a digital twin to replicate realistic burn patients and replace standard large animal pre-clinical testing of burn resuscitation protocols. The third part of this work presents the identification of biomarkers capable of guiding, optimizing, and individualizing burn resuscitation. The UO, the most common endpoint used to titrate burn resuscitation fluid doses, has many limitations as a single variable. Hence, this work aimed to find convenient and reliable biomarkers from arterial blood pressure (ABP) waveform to complement UO in guiding burn resuscitation. Pulse pressure variation (PPV), systolic pressure variation (SPV), and stroke volume variation (SVV) are dynamic indices derived from ABP that have shown promise in hemorrhage resuscitation but are not investigated for different resuscitation paradigms for burn injury. We observed the longitudinal behavior of PPV, SPV, and SVV for 21 porcine subjects with 40% burn injury, which were each either under-resuscitated, adequately resuscitated, or deliberately over-resuscitated. We investigated the features' potential in tracking reference cardiac output (CO) and stroke volume (SV) via linear regression and correlation analysis. PPV, SPV, and SVV showed plausible and statistically different trends for different paradigms. While they performed just as well as UO in tracking CO and SV, their inherent advantage of being available in real-time and their disagreement with UO in determining the subject status suggest that they may potentially complement UO in the hemodynamic assessment of burn patients.