Accurate and interpretable blood glucose prediction is critical for optimizing insulin therapy and preventing adverse events in type 1 diabetes (T1D) management. However, existing deep learning models often lack transparency, limiting their clinical adoption. We introduce a novel neural-symbolic framework that integrates Fourier neural operators (FNOs) with clinical rules to predict 30-minute-ahead blood glucose levels with high accuracy and interpretability. FNOs capture periodic glucose patterns, such as circadian rhythms and meal responses, while differentiable clinical rules encode medical knowledge, providing actionable insights like hypoglycemia risk alerts. Evaluated on the OhioT1DM dataset, our model achieves a mean absolute error (MAE) of 10.2 mg/dL, meeting ISO 15197:2013 standards, and 99.8% Clarke Error Grid A+B coverage, ensuring clinical safety. Leave-one-subject-out cross-validation across 12 subjects demonstrates robust generalization (MAE = 8.85 ± 1.33 mg/dL), addressing concerns about sample size limitations. The model exhibits strong robustness to continuous glucose monitoring (CGM) measurement noise (maintaining ISO compliance at 10% noise) and missing data (5.4% performance degradation at 40% data loss). By generating interpretable outputs, such as trend analyses and risk warnings, our framework supports clinical decision-making, enhances patient trust, and facilitates personalized diabetes care. This work advances medical informatics by combining data-driven and knowledge-driven methods, offering a scalable solution for real-time glucose monitoring and insulin dosing in T1D.