Machine learning has become increasingly important in various fields, including the mechanical industry. API steels being a subgroup of high-strength low-alloy (HSLA) steels have been designed for use in the petroleum industry. In this research, the application of machine learning models to estimate the mechanical properties of API steels was explored. Both non-linear and linear machine learning models were employed to predict the yield strength of API steels. The models were evaluated using different performance metrics on test samples, which produced promising results. Random forest model proved to be effective in estimating the yield strength of API steels with a R2 Score of 0.95. The results exhibit the effectiveness of machine learning techniques in predicting mechanical properties, making them a valuable tool for researchers and engineers in the materials industry