<p>It is widely acknowledged that the dimensions and configurations of burrs, as well as surface quality attributes such as roughness in machined parts, are influenced by multiple factors. These aspects encompass the orientation of the cutting tool, the design of the cutting process, the settings used for cutting, the types of tools, their sizes, coatings, and the interplay between the cutting instruments and the workpieces. Therefore, it becomes clear that burr size cannot be defined as a direct function of straightforward parameters. Predicting surface and edge quality characteristics in milling components, such as burr dimensions and surface roughness, presents a notable challenge. This investigation devised an algorithm to address the identified knowledge deficiency, utilizing an ensemble learning regression technique for accurate predictions related to burr size and surface roughness during slot milling of aluminum alloy 2024. A prediction model centered on performance was established, emphasizing the thoughtful selection and tuning of hyperparameters, given the intricate and non-linear nature of burr development and surface quality elements. The effectiveness of this combined learning regression approach was evaluated through metrics including mean absolute error (MAE), mean squared error (MSE), and the coefficient of determination (<i>R</i>²). The study also examined how operational factors, including cutting speed, feed per tooth, depth of cut, and tool type, influence burr formation and surface properties. The prediction model demonstrated exceptional results with new data.</p>