Earthquakes are natural disasters caused by the movement of the Earth’s tectonic plates due to the release of large amounts of energy. Earthquakes have claimed many lives and resulted in billions of dollars in economic losses. In recent years, studies in the field of seismology regarding the availability of seismic datasets to mitigate these impacts have experienced rapid development. We conducted a literature review on a collection of seismic datasets. High-quality seismic datasets are essential for producing earthquake prediction systems with high accuracy. We provide insight into a type of seismic dataset that contains seismic wave recordings from three components: east–west, north–south, and vertical, from each monitoring station, along with its meta information and the parameters contained therein. The information is structured and arranged chronologically to support earthquake prediction systems. Next, we compare the capabilities of seismic datasets in event detection, phase identification, and onset time picking for P waves and S waves. The results of this research can serve as a reference for researchers in the field of seismology in determining the appropriate seismic datasets for developing earthquake prediction systems using machine learning (ML) and deep learning (DL) models.