One of the most significant developments in seismic hazard assessment and disaster preparedness is the role that Artificial Intelligence will play in the estimation of the aftershock potential immediately following earthquakes. Generally speaking, aftershocks are the smaller earthquakes that follow the main shock of a bigger seismic event, occurring as adjustments of the Earth’s crust take place along the fault line of the primary quake. The study of aftershocks provides insight into the mechanisms of fault mechanics and seismic wave behavior, improving predictive models and reducing uncertainty with respect to future seismic activity. Traditional methods of aftershock prediction involve statistical models and/or historical data and usually carry limited predictive power. The coming-of-age of AI, more precisely machine learning, made it a strong tool to enhance such prediction through real-time analysis of complex seismic data sets. The allowance of the integration of AI into seismic monitoring and response frameworks will provide better estimates of aftershock probabilities to help with emergency response efforts, optimize resource allocation, and ultimately reduce the impact of subsequent seismic events on affected communities. This chapter speaks to the importance of methodologies that will revolutionize aftershock estimation through AI. In this work, we walk through some techniques, such as supervised learning, neural networks, and ensemble methods, and their applications in seismic data analysis. The AI models recognize patterns and correlations, beyond the realm of traditional and manual processing, by ingesting real-time earthquake data, historical seismic records, and geological information. Such models can dynamically update predictions at every new data availability with far better accuracy and timeliness. We further detail how AI integrates into current seismic monitoring systems, those cases in which AI-driven predictions have successfully informed emergency response strategies and risk management practices. We further discuss overcoming challenges in data quality, model interpretability, and computational demands, as success for these AI models is bounded by quality and quantity since training datasets drive the accuracy; poor or biased data will yield unsuccessful, biased predictions. Much more important is the fact that AI in typical seismic monitoring systems should be integrated with due care for transparency and interpretability regarding algorithms to make sure that the predictions are reliable and understandable for human operators.