The study is aiming to identify the use of Machine Learning for detecting anomalous access to healthcare. The EHRs within healthcare store private and confidential data of patients that need strong protection against attacks. The present study is examining how ML can aid in anomalous behaviours in the access patterns alerting the system. The study is making use of explanatory design collecting both quantitative and qualitative data to reach the results. The analysis reveals how ML is capable of driving results and can be improved with the use of effective algorithms and architecture. The companies have been suggested to reduce key features in the ML model for better results.