Rapid　and　precise　quantification　of　economic　losses　post　-earthquake　is　critical　for　crafting　informed　disaster　management　strategies　by　governmental　and　insurance　entities.　This　study　introduces　an　ensemble　model,　constituted　by　Support　Vector　Machines　(SVM)　and　Extreme　Gradient　Boosting　(XGBoost),　tailored　for　quick　and　interpretative　prediction　of　GDP　-related　seismic　loss　assessments,　with　Sichuan　Province　serving　as　the　empirical　backdrop.　The　ensemble　approach,　pre　-trained　for　expediency,　draws　on　a　knowledge　-driven　selection　of　30　features　that　span　seismic　risks,　socio-economic　variables,　and　exposure　factors.　Enhanced　by　feature　scaling　and　Bayesian　hyperparameter　optimization,　the　model＇s　efficacy　is　validated　through　6　metrics　such　as　R　-Squared,　MSE,　and　MAE　etc.　The　incorporation　of　SHAP　values　further　unravels　the　model＇s　decision　-making,　providing　transparency　to　the　often　opaque　computations　of　machine　learning.　This　methodology　offers　a　scalable,　interpretable　framework　that　equips　stakeholders　with　timely　and　accurate　insights　for　risk　mitigation,　ultimately　strengthening　earthquake　resilience.