## 3 Results  
According to the bibliographic search conducted in the four databases, i.e., Scopus, Elsevier, IEEE Xplore, and Web of Science, 254, 29, 39, and 150 articles were found, respectively, totaling 472 works. Subsequently, 188 duplicates were identified and removed, leaving 284 articles for review. Next, articles were selected based on their titles and abstracts, eliminating 220, resulting in 64 works for a full review. Of these, 28 articles met the inclusion criteria, with the most relevant being those that applied a machine learning method for early earthquake magnitude estimation. Given the specific focus of this review, all the works were grouped into two categories--the first addresses the estimation of magnitude as a classification task, and the second addresses it as a regression task. Only five articles fall into the classification group, while the remaining 23 correspond to regression. Table 1 presents the relevant aspects of each work included in this review. Figure 2 illustrates the number of articles published per year. It was observed that 2022 and early 2024 saw the highest publication rates, while 2020 and 2021 registered fewer publications. Two articles were published in 2025. While these data are included in the figure, it is important to note that the comparison may not be entirely fair, as the search was conducted only until March of this year. Figure 3 shows the top eight countries that have contributed to publications on this topic. China leads the list, followed by India and Germany. Countries such as the United States, South Korea, Romania, Chile, and Japan have also made significant contributions to research on using machine learning in early earthquake estimation. Figure 4 presents the journals in which the selected articles were published for this review. Examples include _IEEE Geoscience and Remote Sensing Letters_ with five articles and _Geophysical Journal International_ with three. Journals such as _Artificial Intelligence in Geosciences_, _Seismological Research Letters_, and _Bulletin of the Seismological Society of America_ each contributed two articles. Table A1 provides a list of journals with one article in this review. Figure 1: PRISMA diagram used in this systematic review [37]. Figure 5 highlights the authors with the most publications. [PERSON] has five articles, followed by [PERSON] with three. [PERSON] and M\(\ddot{\text{u}}\)[PERSON] each have two. Table A2 lists the authors with one article in this review. Figure 6 shows the countries of origin for the datasets used in the studies. Japan provides the most widely used datasets, including KiK-net and K-NET, followed by the United States with STEAD [38]. In addition, various studies have used data from China, Italy (INSTANCE) [39], and Chile. Table A3 details cases where multiple datasets or synthetic data, not tied to a specific region, were used. Figure 7 illustrates the most commonly used algorithms for early magnitude estimation. CNNs are the most frequently employed, often in combination with RNNs and Transformers. Support vector machines (SVMs) have also been applied to this task, along with ensembles of ML models and hybrid approaches that combine ML with DL, such as  
Figure 3: Articles published by country. Figure 2: Articles published per year. Figure 6: Country of dataset origin.