Clinical Trial Monitoring is the fundamental necessity to direct high-quality clinical research to guarantee research quality and subject protection according to regulatory standards. It is a sophisticated, innovation-based approach to utilizing a company's data, allowing for better-informed decisions regarding where and how to allocate resources. However, the monitoring plan is grouped into two kinds i.e., on-site and centralized monitoring. But recently, regulators have been urged to take on a risk-based monitoring system, as such sponsors are preferring centralized monitoring from the beginning of the trial. Risk-based monitoring assures the quality of trials by managing risks. Furthermore, the Food and Drug Administration (FDA) and European Medicine Agency (EMA) have embraced the risk-based approach and released a guideline paper on the subject. The FDA's proposed advice from 2013 discusses how to oversee clinical trials using a risk-based approach. Still, on-site monitoring, which is carried out by site visits, and centralized monitoring, which is carried out remotely or offsite, have their self-worth and advantages. Because of the greater complexity of grasping those monitoring procedures, in this review article, we will discuss both monitoring plans with an emphasis on centralized monitoring, which will aid in a better understanding of the entire in a nutshell. Also, we will emphasize the benefits, future, and implementation of Artificial Intelligence/Machine Learning in the risk-based monitoring strategy.