Unmanned Aerial Vehicles (UAVs) are increasingly employed in critical applications such as public safety, logistics, and infrastructure monitoring. As their autonomy grows through Machine Learning (ML) model integration, new challenges emerge related to security, reliability, and model interpretability. Cyberattacks such as GPS spoofing and jamming can compromise UAV navigation systems, while ML models often operate as opaque black boxes, limiting operator trust in high-stakes environments. This paper proposes a diagnostic framework based on the SafeML technique to enhance the trustworthiness of ML-driven UAVs. SafeML applies statistical monitoring using the Empirical Cumulative Distribution Function (ECDF) and Wasserstein Distance to detect Out-Of-Distribution (OOD) data and quantify prediction reliability at runtime. The study evaluates multiple ML models, including Random Forest (RF), LightGBM, and XGBoost, on a UAV dataset featuring real-world GPS spoofing and jamming scenarios. Experimental results show that the best models achieve accuracies above 98%, with SafeML effectively identifying low-confidence predictions that correlate with classification errors.