Soft-wing kites are morphing, bridled, tensile lifting surfaces used for wind-assisted ship propulsion and airborne wind energy applications. Their swept-back planform, pronounced anhedral, and unconventional leading-edge geometry induce complex aerodynamic behaviour that challenges conventional modelling approaches. For leading-edge inflatable (LEI) kites, pressure-side separation induced by the inflated tubular leading edge renders classical inviscid methods insufficient, thereby necessitating sectional input from higher-fidelity approaches. This study presents and applies a computationally efficient aerodynamic framework to an LEI kite by coupling a vortex step method (VSM) with RANS-derived airfoil polars validated against wind-tunnel measurements. The RANS simulations were used to train a machine-learning surrogate model to facilitate parametric design studies. Applying machine learning to LEI kite aerodynamics is novel, and it achieves <em>R</em><sup>2</sup> > 0.98 across the considered parameter space. Three-dimensional load predictions for the TU Delft V3 LEI kite were evaluated against wind-tunnel data and reference three-dimensional RANS simulations. Within the operational incidence range α ∈ [−1,10]°, the predicted lift and drag agree with measurements to within 9 % and 13 %, respectively. Across this range, the framework reproduces the measured aerodynamic trends more consistently than the reference three-dimensional RANS results, while reducing the computational cost by several orders of magnitude. A rigid-body stability analysis indicated static stability in roll, pitch, and yaw, but limited aerodynamic damping within the quasi-steady model. Parametric analyses revealed inherent trade-offs between aerodynamic efficiency and stability, motivating the adoption of multi-objective optimisation strategies. The validated framework provides high predictive accuracy at low computational cost and forms a foundation for rapid aerodynamic analysis, stability assessment, design optimisation, and aero-structural coupling in the conceptual and preliminary design phases.