Manufacturing contributes a significant amount of money to the economy, while consuming nearly one-thrid of the total energy produced within the nation. Computer-aided technologies streamline the development of products, however they lack energy-conscious practices for subtractive manufacturing. With this gap, research has been done in the development of mechanistic and data-driven models to accurately predict energy consumption of this process. However, the majority of research models largely fail to properly validate their work with unrepresentative experimental methodology. In this paper, a data-driven deep learning machine learning model is developed which properly accounts for the complexities associated with CNC machining, as it accounts for variations in operations observed during CNC machining. In this paper, a data-driven machine learning model makes predictions by processing supplemented NC programs, sequentially. These programs include additional information regarding the material removal process. Four variants of the model are created to provide insights into the effects of supplementing the program with different material removal variables. The variables include depth of cut, width of cut, material removal rate, and the volume of material removed per NC instruction.