An ensembling of multiple decision trees on random subsets of the training data followed by a majority vote constitutes the Random forest (RF) . This prediction algorithm is not only popular and widely used but also serves as a benchmark for clinical trial research and has outperformed standard classifiers, such as Logistic Regression . Recent studies on predicting the approval of clinical trials have demonstrated the efficacy of the RF algorithm . Inspired by these precedents, we have used the RF classifier to predict an input clinical trial's success (or failure).