Machinery learning is a fast-expanding area that will change the method for the diagnosis and management of this chronic condition by applying itself to diabetes as a global pandemic. Machine learning principles have been used to build algorithms to help predictive models of the likelihood of diabetes development or related complications. Digital therapy has shown to be a well-established lifestyle care intervention for diabetes control. Patients are becoming more self-managed, and the assistance of therapeutic decision-making is available to both them and health care practitioners. Machine learning helps patient signs and bio-markers to persist, unburdened, remotely controlled. Social networking and online forums also increase patient commitment to the treatment of diabetes. Development in technologies helped to optimize the use of diabetes tools. These smart technological reforms together have led to an improved glycemic regulation, a decrease in fast glucose and glycosylated hemoglobin levels. Machine learning introduces a change in diabetes treatment model from traditional management techniques to data-driven care growth The trouble with medicines is that various drug formulations can cure the condition in several ways. As the diabetic population grows, new medications are increasingly emerging. In order to treat common diseases such as elevated cholesterol and high blood pressure, diabetics also continue to take other drugs. With the patient's age and other physical conditions, the potency of these medicines varies In this method, the effectiveness, risks of side effects and costs are measured side by side, and are readily grasped by doctors and patients. The most prevalent form of Type 2 diabetes effects more people as people grow up. This disease has also escalated dramatically due to the spread of western diets and lifestyles to developing countries. Diabetes is an incurable metabolic illness that happens when high blood sugar is present, and may have deadly effects. Today, medicine, nutritious diets and exercise will regulate diabetes. It is also unpredictable to choose and administer the most appropriate mixture of prescription, which is stable, cheap and well tolerated by patients as well By applying an adequate methodology for the design and development of systems experts can achieve objectives satisfactorily, as in the case of the Weiss and Kuligowski methodology. On the other hand, machine learning has several knowledge machine algorithms, which can be useful to be applied on various data sets through the different interfaces that offers, as the option of Explorer and Datasets, which were worked in this case of study, or to be included in other applications. Furthermore, both tools, contain what is necessary to conduct data transformations, grouping, regression, clustering, correlation and visualization tasks. Because they are designed as extensibility-oriented tools which allows to add new functionalities to a project, because it can be combined with other programming languages such as Prolog, for generation more robust expert systems Readmitted diabetes patients Machine learning techniques allow to automatically identify patterns and even make predictions based on a large amount of data that could be extracted from the computer systems used to ascertain information on readmission of diabetes patients. The analysis Clustering or grouping is a technique that allows exploring a setoff objects to determine if there are groups that can be significantly represented by certain characteristics, in this way, objects of the same group are very similar to each other and different from objects in other groups. The results obtained by comparing the relevance of different attributes as well as the use of two of the most popular algorithms in the world of machine learning are presented: neural networks and decision trees. Automatic classification of blood glucose measurements will allow specialists to prescribe a more accurate treatment based on the information obtained directly from the patients' glucometer (Hosseini et al, 2020). Thus, it contributes to the development of automatic decision support systems for gestational diabetes. This high level of glucose in the blood is transferred to the fetus causing various disorders: excessive growth of adipose tissues, which increases the need for caesarean section, neonatal hypoglycemia and increased risk of intrauterine fetal death (Dagliati et al, 2018). It also increases the risk of type 2 diabetes once the gestation period is over for both the mother and the fetus. The project proposes the development of intelligent and educational tools for the survey based on neurodiffuse techniques integrated into a telemedicine system. Telemedicine systems have been used with success on numerous occasions in diabetes and the integration of decision support tools in this type of system helps a better interpretation of the data (Abhari et al, 2019).