<p>High-carbon-content hypereutectoid steels exhibit improved hardness and wear resistance, making them suitable for high-load applications. However, they have additional brittleness and difficulty in processing. Hypereutectoid steels require more stringent control for microstructure and properties. This investigation enables the exploration of the Spatial Bayesian Neural Network (SBNN) to characterize the mechanical properties of hypereutectoid steels and the interlamellar spacing of eutectoid in hypereutectoid steels. The novelty of this study lies in the combination of spatial dependencies within the model, which allows for more accurate predictions for the composition of alloying and structural data, such as lamellar spacing, compared to conventional approaches. The main objective of the proposed technique is to increase accuracy and reduce uncertainty in the analysis and processing of hypereutectoid steel. This method is implemented and compared to existing methods on the MATLAB platform, such as Radial Base Function Neural Network (RBFNN), Multi-Stage Neural Network (MSNN), and Artificial Neural Network (ANN). The method proposed shows superior accuracy of 98.8% and lower RMSE of 0.241 in predicting mechanical properties and interlamellar spacing of hypereutectoid steels compared to other methods. The developed model enables the optimization of alloy compositions to achieve the perfect balance between strength, plasticity, and lamellar spacing by evaluating the effects of alloying elements as well as the desired combination of strength and plasticity properties. Overall, the proposed method offers a significant improvement over current methods for predicting the mechanical properties and interlamellar spacing for hypereutectoid steels by minimizing error and maximizing accuracy.</p>