Accurately and rapidly simulating the hysteretic behavior of double skin composite wall (DSCW) under earthquake loads enhance the efficiency of seismic performance assessments for high-rise steel-concrete composite structures. The application of artificial intelligence in civil engineering enables a shift from purely physics-based models to methods that integrate physical mechanisms with data-driven approaches. To this end, a machine learning-aided model for simulating the hysteretic behavior of DSCW under earthquake loads is proposed. Firstly, a fiber-based uniaxial material model incorporating softening was proposed to replicate the primary damaging mechanisms of DSCW, validated through comparison with experimental data. A comprehensive dataset covering a broad range of engineering metrics of DSCW was generated, incorporating various loading protocols. The modified Bouc-Wen model was utilized to calibrate hysteretic curves, solving an inverse problem via optimization techniques to determine consistent hysteretic parameters (CHPs) of DSCW. The analysis results confirm the highest accuracy of the artificial neural network (ANN) model. The SHapley Additive exPlanations (SHAP) analysis provided interpretability, highlighting the significant influence of geometric parameters and confirming the interdependence among Bouc-Wen model parameters. The performance of the ANN model was evaluated against a Transformer-based end-to-end deep learning model. The results indicated that the ANN model exhibited superior performance, particularly under asymmetric loading conditions, due to its integration of physical mechanisms into the force-displacement constitutive relationship.