Background: Hemorrhagic transformation (HT) is a critical sequela of acute ischemic stroke (AIS). Matrix metalloproteinase-9 (MMP-9) has been implicated in the pathophysiology of hemorrhagic transformation (HT) post-acute ischemic stroke (AIS). However, the diagnostic accuracy of plasma MMP-9 concentrations as a predictive marker for HT remains inconsistent across studies. Aim: This study aims to determine the potential of plasma MMP-9 concentrations as an indicator for HT in AIS patients. Methods: Systematic reviews of leading databases such as PubMed, Embase, and the Cochrane Library were performed until March 2023. Studies were included assessing the diagnostic accuracy of plasma MMP-9 concentrations in predicting HT after AIS. Statistical analyses were conducted using R software (version 4.0.3) and the mada package. This analytical method enabled the pooling of sensitivity, specificity, false-positive rates, diagnostic odds ratio, as well, as both positive and negative Likelihood Ratios. Outcomes are reported within a 95% Confidence Interval (CI). Results: Our analysis included 4 studies with a total of 378 patients, of whom 79 developed hemorrhagic transformation. Plasma MMP-9 displayed notable predictive capabilities with a pooled sensitivity of 92.4% (95% CI: 77-97.8%, I 2 =0%), highlighting its proficiency in detecting HT cases. Its specificity was 78.3% (95% CI: 73.2-82.6%, I 2 =0%), emphasizing its accuracy in distinguishing non-HT cases. However, there was a false-positive rate of 21.7% (95% CI: 17.4-26.8%). The positive Likelihood Ratio was 4.25 (95% CI: 3.36-5.38), and the notably low negative Likelihood Ratio was 0.09 (95% CI: 0.03-0.32). The diagnostic odds ratio, suggesting strong discriminative power, was 43.72 (95% CI: 11.73-162.91). Conclusion: Our analysis underscores the promising role of plasma matrix metalloproteinase-9 concentrations in predicting hemorrhagic transformation following an acute ischemic stroke. However, the broad adoption in clinical settings mandates further confirmatory studies and validation across heterogeneous populations.