The utilization of Artificial Intelligence (AI) in earthquake prediction, emphasizing its capacity to transform seismic forecasting. The research investigates several AI methodologies, including rule-based systems, shallow Machine Learning (ML), and Deep Learning (DL) techniques, and their utilization in analyzing seismic data and precursory events. Significant focus is placed on Anomaly Detection (AD) techniques, which have demonstrated efficacy in recognizing patterns in geophysical data that may precede seismic occurrences. The study examines the evaluation of precursors, including radon concentrations, geomagnetic variations, and crustal deformations, by applying AI algorithms. Although AI exhibits considerable potential in uncovering concealed patterns and connections within intricate seismic data, obstacles remain. This encompasses the inadequate comprehension of seismic mechanics, the potential for false positives and negatives, and the constraints of existing monitoring equipment. The document examines the amalgamation of many data sources and the capacity of machine learning to enhance the precision and promptness of earthquake forecasts. Notwithstanding persistent hurdles, the study determines that amalgamating AI with conventional seismological techniques offers a viable pathway for enhancing earthquake prediction abilities, potentially resulting in more efficient early warning systems and superior disaster preparedness.