Our paper reviews various applications of machine learning and deep learning models and concepts in the diagnosis of chronic diseases. Patients suffering from these diseases need lifelong treatment. At the moment, Predictive models are frequently applied in the diagnosis and forecasting of these chronic diseases. In this study, we reviewed and analysed the most common chronic diseases. We are mostly focused on chronic diseases like Diabetes, Heart Disease and Skin Diseases. The outcomes of our journal suggest the diagnosis of chronic diseases, but there is no standard method to determine the best approach in real-time medical/clinical practice since these methods have their own advantages and disadvantages. Among the most commonly used methods, we considered Support Vector Machines (SVM), logistic regression (LR), clustering and convolutional neural network. These models are highly applicable in the classification, and diagnosis of chronic diseases and are expected to become more important in medical practice shortly

Keywords : Medical/Clinical Data Analysing, Image Recognising, Convolutional Neural Network (CNN), Disease Prediction Models, Chronic Diseases (Diabetes, Heart Diseases, Skin Diseases), Accuracy of Models.