This study delves into the realm of material science, where the prediction of steel tensile properties has been traditionally reliant on labor-intensive and costly physical testing methods. Recent advancements in machine learning techniques offer a promising alternative by accurately forecasting these mechanical properties, providing a more efficient and economical approach. In an era where precision is paramount in nonlinear contact-based technological processes, the demand for precise workability parameter determination, including initial yield stress, plastic hardening modulus, and true failure strain, is ever-increasing. Machine learning, as demonstrated in this research, has emerged as a robust tool to meet these demands, particularly in the domain of steel properties. The results, prominently featuring the Random Forest model’s high accuracy in predicting tensile strength, yield strength, and elongation, underline the potential of machine learning in replacing conventional and time-consuming physical tests. Moreover, this study revealed that robust data preprocessing is pivotal for reliable predictions. Outliers and missing values have a tangible impact on the predictive accuracy, further underscoring the importance of data management strategies. The implications of this research extend beyond the laboratory, offering practical solutions for industries relying on the mechanical properties of steel alloys. This study adds to the ongoing dialogue on the applications of machine learning in material science and reinforces the viability of these methods in predicting material properties, particularly for steel alloys.