Abstract

Earthquakes are among the most destructive natural disasters, yet accurately predicting them remains one of science’s greatest challenges. Traditional seismological approaches struggle to interpret complex patterns from vast seismic datasets. Recently, machine learning (ML) has shown promise in seismology by identifying hidden patterns, detecting microseismic activities, and forecasting earthquake probabilities. This paper explores the integration of ML into earthquake prediction, reviewing current models, methodologies, and challenges. It also proposes a data- driven framework for improving seismic event forecasting using supervised and unsupervised ML algorithms.