The study of ambient solar wind is an important component of space weather research. The ambient solar wind flows in interplanetary space determine how solar storms evolve through the heliosphere before reaching Earth, and especially during solar minimum are themselves a driver of activity in the Earth’s magnetic field. Accurately modelling and forecasting the ambient solar wind flow is therefore imperative to space weather awareness. Here we present a novel machine learning approach in which solutions from models of the solar corona based on ADAPT magnetic maps are used to output the solar wind conditions some days later at the Earth. The results are compared to observations and existing models for one whole solar cycle in a comprehensive validation analysis, and the new model outperforms existing models in almost all measures. The final model discussed here represents an extremely fast, well-validated and open-source approach to the forecasting of ambient solar wind at Earth.