These barriers have been overcome with the emergence of DL, which is based on essential nonlinear mathematical functions applied to raw data, making some models more efficient at performing classification or regression tasks. In the field of seismology, the exponential growth in data availability and computational processing has enabled the application of ML and DL algorithms to various tasks, including event detection [15; 16; 17; 18; 19; 20], phase detection [21; 22; 23; 24; 25; 26], ground motion estimation [27; 28; 29; 30], and magnitude estimation [3; 4; 7; 31; 32], among other applications. To address the growing demands of seismic data analysis, various deep learning architectures have been employed to enhance the accuracy and efficiency of magnitude estimation. Among them, convolutional neural networks (CNNs) have been widely adopted due to their ability to extract spatial and frequency-based features from seismic waveforms [33]. These networks capture local waveform patterns by applying convolutional layers, making them particularly effective for event classification and magnitude estimation. Additionally, integrating CNNs with attention mechanisms (AMs) improves feature extraction for large-magnitude events, which are often underrepresented in training datasets. While CNNs effectively capture spatial features, seismic signals also contain strong temporal dependencies that require specialized architectures. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) networks, have been employed to model these temporal relationships [4]. Unlike CNNs, which analyze spatial structures, RNNs process continuous time series data, making them well-suited for magnitude estimation and early earthquake classification. These architectures can capture long-term dependencies in P-wave signals, which are crucial for rapid magnitude prediction. Furthermore, multi-station LSTM networks have been developed to aggregate data from multiple seismic sensors, improving regional magnitude estimation and reducing prediction uncertainty. More recently, Transformer-based models have emerged as alternatives to both CNNs and RNNs for seismic applications. Unlike previous architectures, Transformers utilize self-attention mechanisms to learn complex dependencies across entire seismic records [33],This capability makes them highly effective for early magnitude estimation and event classification. For instance, the DFTQuake [34] model incorporates Fourier-based attention layers to improve the accuracy of magnitude estimation by capturing frequency-domain correlations in seismic signals. Additionally, techniques such as low-rank adaptation (LoRA) [33] have been employed to fine-tune Transformer architectures for regional seismic datasets, enhancing their adaptability while maintaining computational efficiency. These improvements offer a promising avenue for real-time seismic analysis, ensuring scalability and generalization across different tectonic environments. Recent advancements in computational capabilities, particularly in GPU-accelerated methodologies, have enabled large-scale simulations for seismic analysis and structural response evaluation [35]. The integration of such high-performance computing strategies with ML-based magnitude estimation can improve both prediction accuracy and response time, facilitating the deployment of EEW systems in high-risk zones. Beyond magnitude estimation, ML and DL models are also being explored to assess secondary earthquake-related hazards. For example, a spatiotemporal casualty assessment method that evaluates the impact of earthquake-caused debris from building clusters, considering human emergency behaviors, has been proposed [36]. These approaches integrate real-time seismic data with urban infrastructure models and human behavior simulations to enhance disaster risk assessments.