Soil liquefaction is one of the most critical geotechnical phenomena that can severely impact the stability and performance of engineering structures during seismic events. Accurate prediction of liquefaction potential and its subsequent effects on structural stability is a complex, non-linear problem influenced by a combination of intertwined geotechnical and seismic parameters. In this research, an Artificial Neural Network (ANN) model was developed to simulate and predict the impact of soil liquefaction on structural stability using a comprehensive dataset of geotechnical and seismic features. To enhance the predictive performance and generalization capability of the neural network, the hyperparameters and network architecture were optimized using the Cuckoo Optimization Algorithm (COA) an algorithm that enables the simultaneous optimization of multiple conflicting objectives, such as prediction accuracy and model complexity. The optimized neural network demonstrated highly superior performance in classifying liquefaction and non-liquefaction states, delivering high accuracy and remarkable stability on the validation dataset. Furthermore, the hybrid ANN–COA framework provides a reliable, efficient, and computational approach for assessing the impact of liquefaction on structural stability, offering valuable insights for seismic design, risk assessment, and the formulation of hazard mitigation strategies in geotechnical engineering.