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Introduction: The lengthy and costly process of drug development can be expedited through drug repositioning (DR), a strategy that identifies new therapeutic targets using existing products. Supervised machine learning (SML) models, incorporating interaction networks, offer a promising approach for DR. This study aims to systematically review and meta-analyze SML models predicting DR, identifying key characteristics influencing their performance. Methodology: A systematic review was conducted to identify SML models that used networks to predict DR, which were evaluated by comparing their performance through a random-effects meta-analysis. Results: 19 studies were included in the qualitative synthesis and 17 in the quantitative evaluation, The Random Forest (RF) model emerged as the predominant classifier (63%), yielding the highest performance in AUC ROC comparisons (overall value: 0.91, 95% CI: 0.86 - 0.96). Validation efforts in 18 studies confirmed the predictions of the SML models, affirming the proposed drugs. The incorporation of chemical structure in model training was found to enhance performance by aiding in prediction discrimination. Conclusion: SML models can predict DR, the RF model was the most widely used SML model with the best performance results, which underscores the potential use of FR models for predicting DR using network form biomedical information. [ABSTRACT FROM AUTHOR]