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Earthquake-induced soil liquefaction (EISL) can cause significant damage to structures, facilities, and vital urban arteries. Thus, the accurate prediction of EISL is a challenge for geotechnical engineers in mitigating irreparable loss to buildings and human lives. This research aims to propose a binary classification model based on the hybrid method of a wavelet neural network (WNN) and particle swarm optimization (PSO) to predict EISL based on cone penetration test (CPT) results. To this end, a well-known dataset consisting of 109 datapoints has been used. The developed WNN-PSO model can predict liquefaction with an overall accuracy of 99.09% based on seven input variables, including total vertical stress (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>σ</mi></mrow><mrow><mi>v</mi></mrow></msub></mrow></semantics></math></inline-formula>), effective vertical stress (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>σ</mi></mrow><mrow><mi>v</mi></mrow><mrow><mo>′</mo></mrow></msubsup></mrow></semantics></math></inline-formula>), mean grain size (<i>D</i>₅₀), normalized peak horizontal acceleration at ground surface (<i>α_max</i>), cone resistance (<i>q_c</i>), cyclic stress ratio (<i>CSR</i>), and earthquake magnitude (<i>M_w</i>). The results show that the proposed WNN-PSO model has superior performance against other computational intelligence models. The results of sensitivity analysis using the neighborhood component analysis (NCA) method reveal that among the seven input variables, <i>q_c</i> has the highest degree of importance and <i>M_w</i> has the lowest degree of importance in predicting EISL.