<p>The importance of aerodynamic characteristics of tall structures in structural engineering, particularly in metropolitan areas where wind can be highly variable, is well understood. This research assessed the wind-induced responses of a G + 25 rectangular RCC high-rise building in two directions (0° and 90°) using codal analysis, numerical simulations, wind tunnel testing, and an integration of advanced AI-based modelling methods. The wind tunnel testing confirmed the consistency of the codal and numerical predictions through validation of the aerodynamic pressure distribution and load transfer mechanism in a 1:150 scaled model. The ETABS models allowed for the prediction of structural responses to varying loading conditions, and the codal wind loads were computed based on the standard of IS 875 (Part 3: 2015). An artificial neural network (ANN) and long short-term memory (LSTM) networks were created to predict displacements and drifts at each storey. The LSTM model showed more accurate successive height-wise fluctuations (R² = 0.9985) than the ANN. The SHAP study was conducted to determine the most important factors affecting the model to improve the interpretability of the model, which included wind speed, building height, and pressure coefficient. The increased wind resistance of the 90° design indicated that orientation significantly affects the performance of a structure. The combined approach fills the gap between the prescriptive rules of wind design and empirical verification of performance-based wind design, enabling the development of high-rise buildings in various Indian wind zones.</p>