Multiple studies in the literature have concentrated on utilising machine learning algorithms to forecast the success of clinical trials ( ). These algorithms are based on structured and unstructured information obtained from clinical studies. These investigations aim to predict the premature termination of clinical trials by utilising trial characteristic data in conjunction with unstructured data. Follett et al. utilised both structured and unstructured data to forecast the discontinuation of clinical trials ( ). The study employed text mining techniques to extract vector features from the studies' description fields and the studies' fundamental aspects. A dataset of 130,000 investigations was compiled by selecting completed and terminated trials using accessible information before the study commenced. Subsequently, they employed machine learning methodologies to forecast results ( ). Also, The study done by Elkin et al. ( ) employed machine learning techniques to predict the termination of clinical trials based on data taken from clinicalTrials.gov ( ). The study produced a dataset consisting of 68,999 studies. A total of 640 features were created by employing document embedding, keyword features, and trial characteristics. Their study aimed to comprehensively understand clinical trial termination and produce accurate prediction outcomes ( ). Machine learning (ML) in clinical trial simulations demonstrates its potential to enhance performance by reducing the time required.