Background: Predicting hypoglycemia in diabetes management remains a substantial challenge, especially for individuals with Type 1 Diabetes (T1D), advanced Type 2 Diabetes (T2D), and prediabetes. Existing prediction systems are largely dependent on continuous glucose monitoring (CGM) data and offer limited accuracy for extended prediction horizons. These systems often fail to account for the complex, individualized physiological variations inherent to each patient. Traditional monitoring methods are primarily invasive and lack the foresight needed for timely intervention. This limitation results in a heightened risk of severe complications and a significant decline in the quality of life (QoL) of patients with diabetes. Therefore, addressing these limitations requires an innovative, personalized, and multimodal approach to enhance the efficacy of hypoglycemia prediction and empower proactive diabetes management. Objectives: The core aim of this research is to develop and validate advanced predictive models for personalized hypoglycemia prediction through three primary domains: (i.) Methodological Development focused on advanced algorithm development, temporal modeling, and the creation of semantic frameworks to capture complex physiological interactions. (ii.) Data Integration and Analysis emphasizes multimodal data integration, the use of non-invasive monitoring approaches, and advanced pattern recognition to enhance the predictive power of the models. (iii.) the Implementation Framework aims at establishing personalization strategies, assessing clinical implementation, and optimizing technological solutions for embedding predictive models into wearable devices. Collectively, these objectives work towards an innovative, personalized, and practical approach to managing hypoglycemia in individuals with diabetes. Methods: This cumulative thesis synthesized findings from five peer-reviewed publications, utilizing data from three complementary datasets: D1NAMO (n=7, Type 1 Diabetes (T1D) patients), BIG IDEAs Lab (n=16, prediabetic individuals), and MIMIC-III (glucose-insulin paired data from 9 518 patients). Key methodologies included shapelet-based feature extraction to identify distinctive physiological patterns indicative of hypoglycemia and semantic integration using ontologies and knowledge graphs for enhanced data context. Both traditional machine learning (ML) and Deep Learning (DL) models, such as Fully Convolutional Network (FCN) and Residual Network (ResNet), were evaluated for their predictive capabilities. Model validation implemented holdout and leave-one-person-out cross-validation. This approach emphasized personalized performance, temporal alignment, and the integration of multimodal physiological signals to ensure robust, individualized hypoglycemia prediction. Results: This research resulted in several key advancements in predictive modeling for hypoglycemia: (1) The FCN achieved 97% accuracy in predicting the time-to-hypoglycemia, extending prediction horizons up to 48 hours; while the ResNet model achieved 94% accuracy, emphasizing the role of model architecture in optimizing prediction capabilities. (2) Temporal analysis revealed critical glucose normalization patterns within a 1–4 hour timeframe before hypoglycemic episodes, underscoring opportunities for preventive interventions. (3) Shapelet-based analysis revealed varying model performances: the three-layered Convolutional Neural Network (CNN) achieved 76% accuracy with heart rate data, while the two-layered CNN model reached 67% accuracy. In comparison, traditional machine learning (ML) approaches showed complementary strengths – Random Forest Classifier (RFC) demonstrated 73% accuracy with heart rate and 69% with breathing rate data, and Support Vector Machine (SVM) achieved 56% accuracy with heart rate and 65% with breathing rate data. These differences in performance demonstrated that advanced architecture optimization is vital for capturing personalized physiological responses. (4) Correlation analyses demonstrated substantial inter-individual variability in glucose-heart rate relationships, with correlation coefficients ranging from -0.4087 to 0.1882, thus highlighting the necessity for tailored modeling approaches. (5) Integration of a semantic framework, utilizing ontologies and knowledge graphs, uncovered previously undetectable patterns through structured representations of patient-specific factors. This structured knowledge representation contributed to improve interpretability and prediction capabilities. (6) Classification models with temporal pattern modeliing, adapted to patient-specific glucose fluctuations achieved accuracy rates ranging from 84% to 99% for different individuals, thus, highlighting the importance of personalization in predictive modeling. Conclusion: This research demonstrated that integrating multimodal physiological data, advanced temporal modeling, and semantic knowledge frameworks significantly enhances the prediction of hypoglycemic events. Also, by employing personalized modeling approaches, predictive accuracy per patient can be improved, enabling timely and patient-specific interventions. These advancements pave the way for transforming hypoglycemia prediction into a proactive and individualized system, ultimately contributing to better clinical outcomes and improved QoL for patients with diabetes.