Large-Scale Travelling Ionospheric Disturbances (LSTIDs) are wave-like ionospheric fluctuations, often triggered by geomagnetic storms, which play a critical role in Space Weather dynamics. In this work, we present a machine learning model able to forecast the occurrence of LSTIDs over the European continent up to three hours in advance. The model, based on CatBoost-a gradient boosting framework-and trained on a humanvalidated LSTID catalogue, uses a diverse set of physical drivers, ranging from geomagnetic indices, and solar wind and activity data, to ionosonde measurements. It results in three distinct operating modes, tailored for scenarios with varying relative costs of false positives and false negatives. In high-risk settings it is crucial to enable researchers and decisionmakers to understand and trust the predictions made by the model. In our case, explainability is ensured through the SHAP formalism, a game-theoretic approach to explaining model output. The validation phase-involving a global evaluation and interpretation step, followed by an event-level validation against independent detection methods-highlights the model's predictive robustness, suggesting its interesting potential for real-time Space Weather forecasting. Depending on the operating mode, we report an improvement ranging from +72% to +93% over the performance of a rule-based benchmark. Our study concludes with a comprehensive analysis of future research directions and actions to be taken towards full operability. We discuss probabilistic forecasting approaches from a cost-sensitive learning perspective, along with performance-centric model monitoring. Finally, through the lens of the conformal prediction framework, we comment on uncertainty quantification for end-user risk management and mitigation.