<p>This study introduces a novel approach to predicting the maximum welding speed in a high-frequency induction welding steel tube line using ANN (artificial neural networks). Induction welding is a complex and crucial process in the industry, directly influencing weld quality, energy consumption, and productivity. Traditional modeling methods require extensive experimentation and specialized knowledge. This study offers a more efficient solution by leveraging actual production data from the steel industry. Two hidden layer MLP (Multilayer Perceptron) models with a Levenberg–Marquardt backpropagation algorithm were developed to predict the maximum welding speed from 3885 sets of process parameters and material characteristics, such as power, input current, chemical composition of the steel, and tube dimensions. The ANN models were compared to traditional multiple linear regression methods, significantly outperforming them by capturing complex data patterns and adapting to varying conditions. The best MLP network achieved an <i>R</i>² of 0.9677, indicating excellent predictive capacity, whereas multiple linear regression achieved an <i>R</i>² of 0.8369. These findings highlight the value of artificial intelligence in modeling complex processes like induction welding, providing a powerful tool to predict welding speed accurately. This approach can support improvements in production efficiency by predicting feasible welding speeds based on historical data, aligning&#xa0;with operational limits observed in practice.</p>