<p>This paper explores the impact of additive manufacturing processes on the properties of stainless steel SS404L, with a focus on the influence of printing parameters such as power, scanning speed, and deposition height on the material's final mechanical properties. The study emphasizes these factors, especially when measurements are taken at a fixed angle of 0º. The data includes these input parameters and the resulting properties, such as density, hardness, and surface roughness of SS404L components made through additive manufacturing. The paper compares two prediction methods, Linear Regression and k-Nearest Neighbors, and finds that Linear Regression is more accurate because it better captures the direct relationships between the input factors and the mechanical properties. This highlights the importance of carefully controlling printing parameters to achieve the desired qualities in SS404L. Looking ahead, using advanced machine learning techniques, like neural networks, could further improve the accuracy of property predictions. Expanding the research to include other types of stainless steel or alloys could provide more insights into optimizing additive manufacturing processes for different materials.</p>