Diabetes, a severe and chronic condition characterized by elevated blood glucose levels, has been a significant health challenge. In recent years, machine learning has shown promise in predicting the identification of diabetes and its related complications. However, the development of these predictive models has been hindered by inconsistencies, poor data quality, inappropriate correlational models, and the inherent complexity of clinical data. These issues often render existing machine learning algorithms for diagnosis and treatment ineffective. In this chapter, we propose a novel survey-based machine learning model for the early detection of diabetes. This model, which has the potential to enhance the effectiveness of existing machine learning algorithms significantly, utilizes the synthetic minority oversampling technique and machine learning techniques, thereby advancing healthcare technology and aiding healthcare professionals in diabetes diagnosis and creating effective prevention strategies.