Predicting hypoglycemic events in a timely and accurate manner is critical for the effective management of Type 1 Diabetes (T1D) and the prevention of its harmful health consequences. This study presents sophisticated predictive models that use noninvasive monitoring technology and shapelet-based feature extraction from heart and breathing rate data to capture physiological signals indicative of hypoglycemic episodes. The core objective was to harness the descriptiveness of non-invasive physiological time-series data with the robust predictive capabilities of machine learning for T1D management. To achieve this, we implemented and evaluated three machine learning classifiers: support vector machines (SVM), Random Forest Classifiers (RFC), and Convolutional Neural Networks (CNN). We also leveraged shapelets to extract distinct, patient-specific physiological patterns indicative of hypoglycemic episodes. The proposed method involves comprehensive preprocessing of physiological sensor data from the D1NAMO dataset, extracting shapelet features characteristic of hypoglycemic events, and iterative classifier optimization modeling. The performance metrics—including accuracy, precision, recall, and the F1-score—were estimated to evaluate each model’s effectiveness.The results indicate that the CNN model outperformed the other models, achieving 76% accuracy, 77% precision, and a 0.76 F1-score for hypoglycemia detection. The SVM and RFC models also demonstrated substantial capabilities, underpinning their potential use in clinical settings. In conclusion, the integration of shapelet-based feature extraction into machine learning frameworks can significantly advance personalized hypoglycemia prediction and demonstrate a promising avenue for enhancing noninvasive diabetes monitoring techniques for patients with T1D, thereby setting the stage for extended research and refinement in this vital healthcare domain.