Automatic classification of seismic-volcanic events is crucial for effective volcano monitoring and risk mitigation. Traditional classification methods, which heavily rely on manual intervention and conventional signal processing algorithms, often face significant challenges in handling large volumes of real-time data with high accuracy. Deep learning techniques have shown great potential due to their ability to learn features directly from raw data. However, the “black box” nature of these models poses a challenge in terms of interpretability and reliability, which is critical for decision-making in seismology and volcanology. This research examines recent advancements in integrating Explainable Artificial Intelligence (XAI) with deep learning models to enhance the classification of seismic-volcanic events. The review highlights the use of techniques such as gradient-based explanations, attention mechanisms, and hybrid models that combine CNN and LSTM with interpretability algorithms. Recent studies have demonstrated that the application of XAI provides greater transparency and trust in the decisions made by artificial intelligence models. Future research should focus on developing XAI techniques that enable more efficient and real-time integration with early warning and decision-making systems.