The prevention of complications associated with hyperglycemia. This study proposes a novel multi-branch neural network model that integrates Long Short-Term Memory (LSTM) networks with a demographic classification model to improve blood glucose prediction accuracy. The model utilizes demographic data to train a neural network, while the OhioT1DM dataset is employed to train the LSTM for forecasting glucose levels. By combining these predictive techniques, the model evaluates both the likelihood of elevated glucose levels and the probability of diabetes based on demographic data. When both predictions indicate a potential risk, the system automatically triggers an alert, thereby facilitating timely clinical interventions. The combined approach demonstrates high accuracy in glucose level prediction compared to traditional models. Furthermore, a user-friendly interface was developed to streamline data entry, allowing for real-time predictions and enhancing the application of the model in clinical settings. Preliminary results indicate that this integrated approach significantly enhances predictive performance and supports personalized strategies for diabetes management.