The potential for AI/ML algorithms to improve the accuracy of diagnosis in intelligent healthcare systems is the subject of this paper. AI/ML algorithms like gradient boosting models, convolution neural networks, and recurrent neural networks can learn from many data to classify, recognize, and predict certain concepts. With the advances in artificial intelligence/ML innovation, the exactness of findings has been significantly moved along. Artificial intelligence/ML calculations have empowered the advancement of brilliant medical care frameworks that are equipped for giving quicker and more precise judgments and results. Intelligent healthcare systems can reduce medical errors caused by human error and produce more accurate results by combining machine learning with human expertise. A case study of an intelligent diagnostics system for health care is presented alongside an examination of the various AI/ML algorithms in this paper.