In the manufacturing sector, which is becoming increasingly automated at all levels, the ability to obtain fast and accurate estimates of processing times is a key requirement for ensuring production efficiency. In milling, machining time estimation is typically derived from toolpath simulations, which, although precise, are time-consuming and strictly dependent on the specific machine setup and cutting strategy. This dependency limits their use in the early stages of production planning and limits flexible manufacturing. This work proposes a deep learning approach based on Graph Neural Networks (GNN) that directly exploits the geometric information contained in STL files to predict milling process times.