Accurate modeling of outdoor Wi-Fi propagation in dense urban environments is essential for smart city connectivity. Deterministic ray-tracing techniques provide high-fidelity insight into multipath propagation but suffer from high computational cost and limited scalability in large 3D environments. This work investigates a hybrid approach that combines MATLAB-based ray-tracing simulations with Machine Learning to enable scalable Wi-Fi~7 network analysis. A large dataset is generated over a realistic simulated university campus, covering multiple frequency bands (2.4, 5, and 6~GHz), transmit power levels, and ray-tracing configurations with reflections and diffractions. Several regression models are evaluated, with emphasis on transformer-based architectures. Results show that the FT-Transformer accurately approximates ray-tracing outputs while reducing inference time from months to minutes. The proposed framework enables fast surrogate modeling of radio propagation and supports network planning and digital twin applications.

Accurate modeling of outdoor wireless propagation in dense urban environments is essential for smart city connectivity. Deterministic ray-tracing techniques provide high-fidelity multipath insight; however they suffer from high computational cost and limited scalability in large 3D environments. This work proposes a hybrid framework combining MATLAB-based (MATLAB 2024b 24.2.0.2773142, 64-bit, 22 October 2024) ray tracing and Machine Learning for scalable Wi-Fi 7 channel analysis. A large dataset is generated over a realistic university campus across multiple frequency bands, transmit powers, and reflection/diffraction configurations. Several regression models are evaluated, with emphasis on transformer-based architectures. The FT-Transformer achieves a Mean Absolute Error (MAE) of $3.49$ dB, RMSE of $5.36$ dB, and an $R^2$ of $99.63\%$ for validation, reducing computation time from months of simulation to seconds at inference. The framework enables accurate and efficient surrogate modeling for network planning and digital twin applications.