We present a new algorithm with an improvement in the accuracy and lead time in short‐term space weather predictions by coupling the Boyle Index, Φ = 10⁻⁴ ν ² + 11.7Bsin³( $\theta$/2) kV, to artificial neural networks. The algorithm takes inputs from ACE and a handful of ground‐based magnetometers to predict the next upcoming Kp in real time. The model yields a correlation coefficient of over 86% when predicting Kp with a lead time of 1 hour and over 85% for a 2 hour ahead prediction, significantly larger than the Kp persistence of 0.80. The Boyle Index, available in near‐real time from http://space.rice.edu/ISTP/wind.html, has been in use for over 5 years now to predict geomagnetic activity. The logarithm of both 3‐hour and 1‐hour averages of the Boyle Index correlates well with the following Kp: Kp = 8.93 log₁₀ < Boyle Index> –12.55. Using the Boyle Index alone, the algorithm yields a correlation coefficient of 85% when predicting Kp with a lead time of 1 hour and over 84% for a 3 hour ahead prediction, nearly as good as when using Kp in the history but without any possibility of “persistence contamination.” Although the Boyle Index generally overestimates the polar cap potential for severe events, it does predict that severe activity will occur. Also, 1‐hour <Boyle Index> value less than 100 kV is a good indicator that the magnetosphere will be quiet. However, some storm events with Kp > 6 occur when the Boyle Index is relatively low; the new algorithm is successful in predicting those events by capturing the influence of preconditioning.