Ensuring secure and reliable communication among unmanned aerial vehicles (UAVs) is a critical challenge in modern wireless networks, especially as drones are increasingly deployed in dynamic, adversarial, and mission-critical environments. This paper introduces a novel framework that leverages neutrosophic logic to model the truth (T), indeterminacy (I), and falsity (F) of UAV communication links, providing a mathematically robust approach to capture uncertainty and risk in real time. By integrating these neutrosophic values as features for advanced machine learning models—including XGBoost, deep neural networks (DNN), and hybrid architectures—the proposed system achieves high accuracy, adaptability, and explainability in classifying secure versus insecure links. The framework further incorporates online learning for real-time adaptation and SHAP-based explainability to enhance transparency in decision-making. Comparative evaluations demonstrate that neutrosophic-based modeling outperforms traditional fuzzy and binary approaches, particularly under noisy and uncertain conditions. The results are validated through both simulated and real-world datasets, confirming the practical relevance and robustness of the approach. This work positions neutrosophic logic, in synergy with machine learning and explainable AI, as a powerful foundation for next-generation secure UAV communication systems and aligns with recent advances in physical-layer security, and privacy-preserving protocols in UAV networks