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Fully printed vanadium dioxide (VO ₂ ) based Radio Frequency (RF) switches have been recently developed for advanced frequency-reconfigurable RF electronics. A reliable and versatile model for the VO ₂ switches is required for design and simulations in the modern Computer-Aided Design (CAD) tools. This paper proposes a machine learning (ML) based model for VO ₂ RF switches, which is much more time and resource efficient as compared to the traditional device models. The computational efficiency, accuracy and robustness of the proposed model over a frequency range of 30 GHz is demonstrated through an excellent agreement between the modelled and measured results. The comparison between the measured and modelled results demonstrate a mean-square error (MSE) of lower than 5 × 10 ^-4 and 5 × 10 ^-3 for the magnitude and phase values over the complete frequency range.