Earthquakes are among the most destructive natural phenomena, leading to significant loss of human life and substantial economic damage that severely impacts affected communities. Rapid detection and characterization of seismic parameters, including location and magnitude, are crucial for real-time seismological applications, including Earthquake Early Warning (EEW) systems. Machine learning (ML) has emerged as a powerful tool to enhance the accuracy of these applications, enabling more efficient responses to seismic events of different magnitudes. This systematic review aims to provide researchers and professionals with a summary of the current state of ML applications in seismology, particularly on early earthquake magnitude estimations and related topics such as earthquake detection and seismic phase identification. A systematic search was conducted in Scopus, ScienceDirect, IEEE Xplore, and Web of Science databases, covering the period from early 2014 to 7 March 2025. The search terms included the following: (“earthquake magnitude” OR “earthquake early warning”) AND (prediction OR forecasting OR estimation OR forecast OR classification) AND (“machine learning” OR “deep learning” OR “artificial intelligence”). Out of the 472 articles initially identified, 28 were selected based on pre-defined inclusion criteria. The described methods and algorithms illustrate the strong performance of ML in earthquake magnitude estimation despite limited implementation in real-time systems. This highlights the need to develop standardized benchmark datasets to promote future progress in this field.