Analysis of 103 acute myocarditis patients across two cohorts identified specific protein signatures that predict disease severity. The study found elevated levels of T-cell cytokines alongside dysregulated fibroblast-derived proteins, particularly bone morphogenic protein 4 (BMP4) and its inhibitors Gremlin-1 and Gremlin-2. Machine learning analysis pinpointed CXCL10 chemokine and GREM2 protein, combined with left ventricular ejection fraction, as the most reliable markers for identifying severe cases. This represents a significant advance for myocarditis management, where clinicians currently lack reliable biomarkers to distinguish patients at risk for cardiogenic shock from those with mild chest pain. The dual T-cell and fibroblast signature suggests the inflammatory cascade triggers both immune activation and cardiac remodeling simultaneously. However, this preprint awaits peer review, and validation in larger, diverse populations will be essential. The findings could transform emergency cardiology by enabling rapid risk stratification through blood tests rather than waiting for imaging results. If confirmed, these biomarkers might guide early aggressive treatment decisions and prevent progression to heart failure.