Hot-rolled strip steel is an essential material extensively used in various industrial fields, with its mechanical properties being critical to product quality and engineering design. This article presents a method for predicting the mechanical properties of hot-rolled strip steel using the NGBoost (natural gradient boosting) algorithm. The study focused on predicting tensile strength, yield strength, and elongation of hot-rolled strip steel and compared the predictive results with those obtained from the gradient boosting algorithm, Lasso regression, and decision tree algorithms. The results indicated that the NGBoost algorithm performs well on average coverage error (ACE) and prediction interval absolute width (PIAW) values at different confidence levels, demonstrating strong predictive performance. Furthermore, the analysis of variance (ANOVA) method was employed to identify factors that significantly impact mechanical performance, providing theoretical support for optimizing design schemes and enhancing structural safety and reliability.