High-frequency induction welding is a practical welding technique widely used in various industries. Although it is generally fairly robust, high-frequency induction welding of aluminum tubes is complicated by the very high line speed, which requires high and accurate power input and therefore, a small fluctuation or variation in power input could result in drastically different welds. This work is dedicated to analyzing the influence of welding parameters, line speed and power input, and especially those induced by the change of aluminum stock, adjustment/maintenance of the induction welding coil, and other unknown random factors. Through machine learning (ML) process, statistical models defining the normal operation windows were developed based on experimental data. The operation window, defined by the overheat-normal and normal-cold boundaries, are expressed in terms of probabilities of producing normal welds. These trained models can be used to make new predictions, i.e., new operation windows by collecting a small sample (a very limited number of calibrating points). This procedure was verified experimentally.