Soil liquefaction, triggered by increased porewater pressure, poses a significant threat to infrastructure stability in seismically active regions, and its forecast remains challenging due to intricate nonlinear interactions. This study utilizes a dataset of 540 samples, incorporating seismic parameters and Standard Penetration Test (SPT) results to evaluate liquefaction prediction. SPT N60 values, soil fine content (FC), ground water table (GWT), effective stress of overburden (ESO), peak ground acceleration (PGA), and earthquake magnitude (Mw) are key inputs. A Gradient Boost Classifier (GBC) machine learning (ML) model was utilized to classify liquefaction events. The performance of the model was assessed using metrics including accuracy, recall, precision, F1-score, and confusion matrix analysis. According to sensitivity analysis, feature importance ranking, and Shapley Additive Explanations (SHAP), the most significant variables in predicting liquefaction were PGA, SPT-N60, and GWT. The robustness of the GBC model was further validated through precision-recall curves and k-fold cross-validation, and it achieved an impressive 99.38% prediction accuracy. These results highlight the potential of GBC technique to advance the reliability of liquefaction forecasting.