This review investigates the application of neural network algorithms in forecasting aftershock dynamics to enhance seismic resilience in structural engineering. Artificial neural networks (ANNs) offer a promising approach for modeling aftershock sequences and predicting their impact on structures, thereby addressing the critical need for accurate and robust seismic evaluations. The study emphasizes the importance of integrating mainshock-aftershock sequences in seismic assessments and highlights the challenges associated with accounting for aftershock influences in structural design. Various methodologies leveraging ANNs are analyzed, accompanied by case studies demonstrating their predictive accuracy and practical benefits. The review underscores key aspects such as advanced data processing techniques, cutting-edge sensor technologies, and the identification of unique frequency patterns essential for precise seismic analysis. The significance of post-yield stiffness ratios, displacement metrics, energy distribution, and plastic hinge behavior in structural performance assessment is also discussed. By synthesizing current research, this review aims to advance seismic engineering practices, enhance structural resilience, and promote safety in earthquake-prone regions.