In this paper, we present novel research that leverages machine learning (ML) models and techniques to automate the outcome prediction of clinical trials. Our study is motivated to combine two crucial aspects, namely, the streamlined selection process of the site of action for a new drug and the optimization of patient enrolment in clinical trials. This unique combination provides an end-to-end solution to proceed with Phase 1 of clinical trials, effectively addressing the limitations that can impede the success of the trial process. By improving the target site selection process, the probability of successful completion of clinical trials increases with minimum system time and spent resources¹ of pharmaceutical companies and researchers, in addition to ensuring the improved safety of patients enrolled in the trials. The model presented in this paper not only enhances the site selection process but also aims to streamline the patient enrolment process, directly targeting the challenges associated with low accrual rates and enrolment inefficiency reported in global statistical analyses of terminated trials within clinical trials databases.² The empirical results derived from our model are presented, demonstrating its efficacy in addressing these critical issues and providing a comprehensive solution for enhancing the efficiency and success rates of clinical trials.