In recent times, the application of Artificial Intelligence (AI) has become widespread across various fields, including machining operations like milling, drilling, shaping, turning, grinding, counter drilling, and counter sinking. Among the techniques used in these applications, Artificial Neural Networks (ANN) have gained popularity. This study focuses on utilizing ANN to optimize drilling hardened non-shrinking (OHNS) die steel. The methodology presented in this research involves the implementation of ANN to analyze the impact of drilling process parameters on surface roughness. MATLAB 2023 software was used to effectively train the neural network. The dataset for this study consists of input and output expressions derived from experimental analysis. The input data includes variables such as cutting speed, feedrate, drill size, and depth of cut. The objective of this study is to predict surface roughness based on input datasets, with 70 of the samples allocated as training data and the remaining 30 for testing and validation. The experimental results demonstrate that ANN provides a reliable prediction capability, with Coefficient of Determination (R2) and Mean Square Error (MSE) values of 0.997 and 0.231893, respectively. The low MSE value indicate better model performance, also the R2 value obtained indicate a strong correlation between the predicted and actual values, suggesting that the ANN model provides a satisfactory fit to the data, indicating a high level of confidence in the statistical results. Thus, it can be concluded that the results obtained from the ANN model are statistically significant, slightly superior to the classical model, and exhibit a good fit. © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)