Background <p>Landslides pose significant risks to lives and infrastructure. This study developed landslide susceptibility mapping (LSM) for Yilan County, Taiwan, a region highly affected by seismic activity and intense rainfall. A comprehensive inventory of 12,047 landslides (2004–2017) and 12 causative factors spanning topography, hydrology, geology, and seismicity were compiled. Three multivariate statistical methods, Analytical Hierarchy Process (AHP), Frequency Ratio (FR), and Simple Additive Weighting (SAW), were applied and validated using Area Under the Curve (AUC) and a suite of error metrics.</p> Results <p>The dataset was partitioned into 70% training and 30% testing samples. Multicollinearity analysis confirmed that all causative factors (Pearson correlation &lt; 0.7, variance inflation factor &lt; 10) were suitable for inclusion. Chi-square ranking identified slope gradient and annual rainfall as the two most influential factors. The Jenks natural breaks method classified susceptibility into four levels: low, moderate, high, and very high. Validation results showed that AHP achieved the highest AUC (training: 0.830, testing: 0.839), followed by FR (training: 0.828, testing: 0.828) and SAW (training: 0.824, testing: 0.814). AHP also yielded the lowest error values (mean squared error = 1.7 × 10⁻³, root mean square = 2.7 × 10⁻²) and the highest correlation coefficient (<i>r</i> = 0.9974). Although differences among methods were relatively small (maximum AUC difference of 0.025), AHP consistently outperformed the others across all metrics. High and very high susceptibility zones, comprising approximately 37.5–49.5% of the study area depending on the method, were concentrated in southern Yilan, particularly in Tiansongpi Township, where steep slopes exceeding 30° coincide with annual precipitation of 3100–4700&#xa0;mm.</p> Conclusions <p>This framework provides a practical and reproducible approach for landslide hazard assessment and risk management in similar geological settings worldwide.</p>