An earthquake is a natural event that causes serious intimidation to infrastructure and humans� life in northern Pakistan. Environmental, physical, and social dimensions effectively add to seismic vulnerability. The current study deals with seismic susceptibility by integrating various decisive supporting methods to generate more accurate outcomes in the Abbottabad District, Pakistan. Hybrid models: fuzzy-logistic regression (fuzzy-LR) and multi-criteria evaluation�logistic regression (MCE�LR) trained at 70 by multiple criteria decision analysis�multi-criteria evaluation (MCDA�MCE) and fuzzy-multiple criteria analysis (fuzzy-MCDA) are used to build hybrid training datasets at 30. High accuracy in the MCDA�MCE model is observed based on the model output. Seismic susceptibility maps are generated by implementing the resulting datasets and hybrid learning models (fuzzy-LR and MCE�LR). Finally, the area under the curve (AUC) and frequency ratio (FR) validate the outcomes of seismic susceptibility maps. In comparison, both MCDA�MCE hybrid model (AUC = 0.812) and MCE�LR hybrid model (AUC = 0.875) indicated more precision than fuzzy-MCDA model (AUC = 0.806) and fuzzy-LR hybrid model (AUC = 0.842), respectively. The current study concludes that training datasets are the responsible factor for defining the seismic susceptibility mapping and modelling accuracy. Moreover, this study helps to specify the high susceptible locations in the urbanized environment and facilitate policymakers to implement measures in the study area for better planning in future to avoid the effects of the earthquake. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.