Being able to predict the cutting forces during milling with a ball-end milling cutter is very important for determining the optimal cutting parameters in the milling process. The already developed models of cutting forces in ball-end milling are based on analytical methods and are determined by means of theoretical and practical knowledge as well as experiments. This paper presents the development of a genetic model of cutting forces for a ball-end milling cutter using artificial intelligence (genetic programming). In the genetic model, all the parameters influencing the size of the cutting forces during the milling process are considered. The presented model is generated from experimental data for Ck45 steel with different cutting parameters. The results indicate that the genetic model of the cutting force agrees with the experimental data.