## 5 Conclusions  
Applying ML to early earthquake magnitude estimation is emerging as a transformative tool in modern seismology. In recent years, this field has experienced significant advancements, driven by the increasing availability of high-quality seismic datasets and enhanced computational capabilities. The findings of this review confirm that DL models, particularly CNN, RNN, and Transformer architectures, have demonstrated high accuracy in magnitude estimation using the first seconds of P-wave data. However, several critical limitations remain. One of the most pressing challenges is data scarcity for large-magnitude earthquakes, which not only increases uncertainty but also contributes to systematic underestimation in extreme events due to saturation effects in waveform features. Moreover, geological variability and seismic network heterogeneity introduce substantial generalization barriers, as models trained in specific regions often struggle to adapt effectively to areas with different tectonic characteristics. Addressing these issues requires advanced domain adaptation strategies, including transfer learning, fine-tuning techniques, and physics-informed corrections, to improve model robustness across diverse seismic environments. Another key aspect influencing model performance is the quality of input data, including SNR, precise signal alignment, and waveform preprocessing methods. Incorporating noise augmentation and advanced preprocessing techniques, such as wavelet denoising and time-frequency domain transformations, has proven effective in enhancing model resilience against distorted signals and improving generalization capabilities. Additionally, data augmentation techniques have been essential in addressing dataset imbalances, particularly for small-magnitude events, which are often overrepresented in training data. One of the most promising applications of these technologies lies in EEW systems. Successfully integrating machine learning models into operational seismic networks has the potential to  
Figure 9: Graphical representation of a real-world usage scenario as a part of an EEW system. enhance disaster response strategies, reducing earthquake impacts on vulnerable communities. However, real-world validation remains imperative to ensure these models meet the reliability and precision standards required for critical applications. Interdisciplinary collaboration among scientific seismologists, engineers, and data scientists is essential to address computational limitations, scalability concerns, model interpretability, and future implementations in real-time. This systematic review comprehensively analyzes machine learning-based approaches for earthquake magnitude estimation. Various architectures have been compared, evaluating their advantages and limitations concerning data availability, computational efficiency, and generalization capabilities. Additionally, key challenges and strategies for improving model robustness in real-time applications have been identified. These findings are crucial for future research and implementing ML-based models in seismic monitoring networks. Conceptualization, E.I.-G. and O.A.C.-A. ; data curation, O.A.A.-C. and C.A.L.-M.; formal analysis, E.E.G.-G. and U.J.T.-P.; funding acquisition, E.I.-G.; investigation, A.N.-R. and O.A.C.-A. ; methodology, A.N.-R., O.A.C.-A. and E.I.-G.; project administration, E.I.-G.; resources, E.E.G.-G. and U.J.T.-P.; software, A.N.-R. and O.A.A.-C.; supervision, E.I.-G. and O.A.C.-A. ; validation, C.A.L.-M., E.E.G.-G. and U.J.T.-P.; visualization, A.N.-R. and O.A.C.-A. ; writing--original draft, A.N.-R. and O.A.C.-A. ; writing--review and editing, O.A.C.-A. and E.I.-G. All authors have read and agreed to the published version of the manuscript. This research was funded by the Universidad Autonoma de Baja California (UABC) through the 25 th internal call for research projects with grant number 402/6/C/53/25, and Centro de Investigacion Cientifica y de Educacion Superior de Ensenada (CICESE).