Artificial intelligence is benefiting modern society in forecasting weather, detecting fraud, recognizing faces, deciphering genomics, and breast cancer but broadly speaking this role in medical practice, especially in SMDM is still unanswered. Shared Medical Decision-Making (SMDM) is an interaction model between patient and healthcare professionals. It is a mutual agreement between a patient, provider, and other healthcare professionals, based on which further treatment decisions are taken. SMDM helps in determining the probabilities about the outcome of the severity of the disease. Through various research, it has been identified that SMDM can be used in the early detection of some chronic diseases, which in the future may lead to a severe condition if not treated at the early stage. According to international guidelines in nephrology, it is suggested that patients suffering from Advanced Kidney Disease (AKD) can take preventive measures well in advance before AKD heads toward End-Stage Kidney Disease (ESKD). Research on diabetic medication adherence through patient-centered collaboration has proved to be one of the effective measures in avoiding hospitalizations. We make a critical review before we go for the implementation of proposed SMDM model in healthcare with the integration of AI. We have underlined the present concerns and components that limit the adoption of shared decision-making from the patient and provider level. It has been noticed that decision and communication aids are one of the effective measures to expedite the instrumentation of SDM in clinical practice. Additionally, health literacy and lack of time during clinical consultation also have a direct impact on SDM. On the other side, AI plays a prominent role to understand the pattern in certain areas like image analysis in radiology, pathology, and dermatology. Due to the sensitiveness of medical decision-making, it is important to go into depth before decision-making. Based on the steps of SDM, we proposed shared medical decision-making with the integration of artificial intelligence techniques that can be helpful for future decision-making. A combination of machine plus physician reliably enhances the overall process of SMDM.