With the advance of technology in recent decades and the emergence of tools aimed at improving quality of life, an opportunity has been identified to improve the management of chronic diseases, especially diabetes. The growing incidence of adverse events associated with the use of glucose meters, as recorded by the FDA’s MAUDE, highlights the need for safer and more effective tools. In a period of a few months in 2018, thousands of adverse events were reported, including measurement failures and significant errors. This paper discusses the development of a prototype digital platform for chronic disease management, using diabetes patients as a case study. The platform integrates augmented reality (AR) and machine learning (ML) to offer more precise and personalized care. The application will function as an interactive community that offers users assistance on the use of equipment and access to educational materials. The AR component simplifies the use of the glucometer by providing visual and interactive instructions. In addition, the platform uses ML algorithms to analyze data, which identifies patterns and correlations between patient characteristics and adverse events. The results deliver a successful prototype, with ML achieving performance above 70%, demonstrating potential for future applications. AR interprets the results and adjusts the instructions for use, which enables personalized and predictive recommendations, improving the user experience. By combining these technologies, the platform aims to facilitate the daily management of chronic diseases, as well as creating a community of support and information exchange, promoting a better quality of life for patients.