In recent years, diabetes, one of the most prevalent diseases globally, has become a hazard to human health on a global scale. This necessitates ongoing proactive monitoring, such as using wearable/smart watches and a variety of smartphone sensors. In addition to being less intrusive, these devices can track a variety of physiological markers critical for diabetes prediction. Emerging technologies, such as AI and machine learning algorithms, are two of the most essential tools for predicting or identifying diabetes based on a variety of physiological signs. This paper focuses on different sensors that play a vital role in collecting different parameters from the human body and develops a web application to monitor diabetes in real time. The suggested framework makes use of various machine learning classifiers for predicting diabetes. It then uses a Large Language Model, such as ChatGPT, to provide recommendations so that the patient can adjust their lifestyle and prevent their health from declining further.