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Snow avalanches cause economic losses in many parts of the world, especially in mountainous areas. Due to its changing climates and the complex topography of mountainous regions in Iran, avalanches are significant problems. Therefore, modeling snow avalanches to predict them is essential for sustainable and safe development, appropriate infrastructure, planning for residential development, and loss prevention. This study assesses the performances of multi-criteria decision-making models and machine-learning hybrid models for snow avalanche susceptibility mapping in northwestern Iran's Sirvan watershed based on the weight of analytic network process, weighted linear combination, and frequency ratio. Using multicollinearity analysis and Pearson correlation, eleven snow avalanche conditioning factors were deemed appropriate for use. The areas under the curve (AUC), root mean square error (RMSE), seed cell area index, and snow avalanche relative index were used to validate snow avalanche susceptibility maps. The results showed that all hybrid models performed very well for the determination of the susceptibility of locations to avalanching. Based on the results of the validation analyses, the analytic network process-frequency ratio hybrid model was best among the models (AUC = 0.941, RMSE = 0.3). The resulting maps provide strong evidence to guide efforts to prevent the exposure to snow avalanches in the Sirvan watershed, and these hybrid models can be tested in other regions affected by snow avalanches. • Variables importance of topographic, meteorological and earth by the MCDA technique. • Snow avalanche susceptibility mapping (SASM) using ANP-WLC, ANP-FR, FR-CTA and FR-SOM novel ensemble algorithms. • Models were validated based on ROC, MSE, RMSE, SCAI and SARI methods. • The results showed that the ANP-FR hybrid model has better predictive performance • Nearly 7% of the study area has high susceptibility to snow avalanche. [ABSTRACT FROM AUTHOR]