### Machine Learning and Artificial Intelligence Applications  
Many studies have shown that AI methods, particularly ML techniques, can increase the accuracy of earthquake forecasts and improve post-event assessments. One application of AI in earthquake prediction involves using artificial neural networks (ANNs). Research indicates that ANNs can analyze large datasets of seismic activity to detect patterns that may signal impending earthquakes. For instance, [PERSON] et al. [10] suggest that training ANNs on global datasets leads to improved predictive capabilities compared to those based on local datasets, highlighting the value of comprehensive data for enhancing prediction accuracy. [PERSON] et al. [11] stress that hybrid models combining various ML techniques, such as the flower pollination algorithm with extreme learning machines, can improve the accuracy of earthquake magnitude predictions. This indicates that integrating diverse AI approaches can create more reliable predictive models. Beyond prediction, AI has been applied to assessing earthquake-induced damage. For example, explainable AI (XAI) has been explored to map damage following seismic events automatically. [PERSON] and [PERSON] [12] observe that although AI has been used in damage mapping, its full potential in evaluating earthquake-induced damage remains untapped. Similarly, post-earthquake assessments using convolutional neural networks (CNNs) have successfully identified collapsed structures via satellite imagery, showcasing AI's capacity to evaluate disaster impacts [13]. AI applications extend to analyzing aftershock patterns and assessing seismic vulnerability. [PERSON] et al. [14] use ML to improve forecasts of aftershock locations, enhancing preparedness and response strategies. The studies utilizing ANNs to evaluate urban vulnerability to earthquakes suggest that AI can contribute meaningfully to urban planning and disaster risk reduction [15]. The expanding role of AI in earthquake science is further demonstrated by the development of frameworks for federated learning, which facilitates collaborative model training across institutions without the need for direct data sharing [16]. Several studies highlight the importance of ML algorithms for earthquake detection. For example, [PERSON] et al. [17] employ a CNN-RF model to estimate earthquake epicenters and magnitudes with low error rates. Similarly, [PERSON] et al. [18] use a Random Forest Classifier to differentiate between earthquake and non-earthquake vibrations, achieving a classification success rate of 78.89%. [PERSON] et al. [19] propose a CNN-based method to classify earthquake-related tweets, utilizing crowdsourced labeled data to identify informative tweets and detect real-time earthquakes. A notable ML application involves neural networks and support vector machines (SVM). For example, [PERSON] et al. [20] present a hybrid model combining support vector regression with neural networks, achieving high accuracy in predicting earthquakes in the Hindukush region. Similarly, [PERSON] et al. [21] designed the NESTORE algorithm to forecast strong aftershocks in Greece, utilizing a dataset spanning nearly 30 years. These examples demonstrate the ability of machine learning to analyze complex seismic data and produce reliable forecasts. Combining various ML techniques has been shown to improve predictive accuracy even more. [PERSON] et al. [22] demonstrate that combining various seismological parameters with ML methods such as logistic regression and SVM produced better results than single-method models. This complements findings that emphasize the importance of deep learning (DL) in extracting seismic patterns from extensive datasets, supporting the idea that hybrid models can outperform traditional approaches. Recent developments in ML have also enabled real-time monitoring and prediction of earthquake-related phenomena. [PERSON] et al. [23] introduce an ML model capable of predicting ground motion in real-time during large earthquakes, achieving a notable accuracy rate of 99%.