Every year, a variety of natural disasters such as fires, earthquakes, and floods occur all over the world, which emphasizes how important it is to have risk mitigation methods that are thorough. In particular, earthquakes stand out as the greatest threat since they have a significant effect on people, communities, and countries, leading to significant economic hardships. With an emphasis on the seismic terrain of Turkey, this research focuses on the critical examination of Artificial Intelligence (AI) models designed for earthquake prediction. Unfortunately, there doesn’t seem to be much information in this field already, therefore more research is necessary. Two different models were examined in the quest for efficient earthquake prediction: Back Propagation Neural Networks (BPNN) and Linear Regression (LR). The results of this investigation showed significant differences in these models’ ability to forecast. After undergoing a thorough evaluation, the LR model performed admirably, obtaining a success percentage of 70.9%. On the other hand, the BPNN model demonstrated a significant edge, with an astounding accuracy rate of 96.8%. As such, the empirical data emphasizes the enhanced effectiveness of the BPNN model for earthquake prediction in the Turkish environment. This large discrepancy in accuracy rates supports the claim that, among the models examined, the BPNN model proves to be a more effective instrument for earthquake prediction in the area of interest.