Myocardial infarction (MI) is a leading cause of death worldwide and the most common precursor to heart failure, even after initial treatment. Precise evaluation of myocardial injury is crucial for assessing interventions and improving outcomes. Extensive evidence from both preclinical models and clinical studies demonstrates that the extent and severity of myocardial injury (i.e., myocardial infarct size, ischemic risk zone, and no-reflow area) are critical determinants of long-term outcomes post-MI. This study aims to assess whether carotid pressure waveforms, analyzed using an intrinsic frequency (IF)-machine learning (ML) approach, can accurately quantify myocardial injury sizes: myocardial infarct size, ischemic risk zone, and no-reflow area. Acute MI was induced in N = 88 Sprague-Dawley rats using a standard coronary occlusion/reperfusion model. MI-injury sizes were obtained via histopathology. IF metrics were extracted from carotid pressure waveforms post-MI. ML classifiers were developed using 66 rats and externally tested on 22 additional rats. Our best developed model for infarct size achieved an accuracy of 0.95 (specificity = 0.95, sensitivity = 0.96). For the ischemic risk zone, the best model showed an accuracy of 0.85 (specificity = 0.90, sensitivity = 0.80), and for the no-reflow area, we reached an accuracy of 0.88 (specificity = 0.89, sensitivity = 0.86). To conclude, a hybrid physics-based ML approach applied to carotid pressure waveforms successfully classified MI-injury severity. As carotid pressure waveforms can be measured non-invasively and remotely (e.g., via smartphones), this proof-of-concept preclinical study suggests a translational potential for post-MI management, enabling timely interventions, improved patient monitoring, and mitigating adverse outcomes.