Abstract: Currently, machine learning and artificial intelligence (AI) methods are predominantly applied to the research and development of high-entropy alloys. Although high-entropy alloys exhibit superior properties and significant industrial potential, their manufacturing costs remain prohibitive, and established industrial applications are currently limited. Conversely, the vast majority of steel products are low-entropy alloys primarily composed of Fe, C, and trace amounts of Mn, Cr, Ni, Nb, and other elements. Presently, Chinese iron and steel enterprises rely largely on traditional “trial and error” methods to develop new steel grades. This traditional “research and imitation” process requires time and high research and development (R&D) expenditure. To enhance R&D efficiency, the utilization of AI methods to facilitate new product development, optimize existing process parameters of existing products, and improve product quality is a critical technological requirement for the industry. This paper proposes a novel product development model based on AI methods, utilizing IF steel R&D as an industrial case study. Given the high-dimensionality, strong coupling, and nonlinearity characteristic of industrial production data, eight machine learning models were evaluated to predict the mechanical properties of IF steel. To ensure model generalization, the data was regularized, the super-parameters were optimized, and the training¹ set underwent five-fold cross-validation. The prediction accuracy and applicable scenarios for each model are subsequently discussed. Among the eight models for predicting the mechanical properties of interstitial-free (IF) steel, the random forest (RF) and deep neural network (DNN) models demonstrated superior accuracy, with R² > 0.97. Furthermore, the kernel principal component analysis (KPCA) model was employed to reduce the dimensionality of high-dimensional components and process parameter features into two-dimensional principal component vectors. A material fingerprint was then established using the Gaussian mixture module (GMM) model. This fingerprinting technique facilitated the visualization of high-dimensional feature data, enabling the analysis and observation of data distributions and the identification of potential mining intervals and the spatial location of the generated data. These potential spaces represent optimal search intervals for the discovery of novel materials. Finally, a generative model based on Wasserstein auto-encoders (WAE) was proposed to explore the potential composition and process parameter spaces. The WAE model can generate thousands of potentially valuable samples from which specific candidates can be selected for industrial testing. This digital “trial and error” approach serves as a robust alternative to traditional methods, accelerating product development while significantly reducing R&D costs and durations.