Accurate prediction of heat partition during milling is essential for optimizing tool life, surface integrity, and process efficiency. However, conventional methods either require high computational effort or provide limited local temperature data, especially under complex milling conditions. This study presents a combined experimental–analytical framework for thermal analysis in milling. A custom-designed test bench enables quasi-two-dimensional milling with orthogonal cuts and allows high-speed thermographic imaging of the tool and workpiece. An analytical model employing a parabolic heat source is used to simulate the temperature distribution in both components. The model incorporates material-specific thermophysical properties and is calibrated using inverse analysis based on experimental temperature measurements. Validation results show close agreement between simulated and measured thermal fields, confirming the reliability of the method. The findings demonstrate that cutting speed significantly affects workpiece temperature by increasing thermal power input, while the feed rate mainly influences heat carried away by the chip. The proposed framework enables efficient and accurate estimation of heat partitioning with minimal computational cost, offering a practical tool for thermal process optimization in milling. The method is particularly suitable for rapid evaluation of cutting conditions and could serve as a foundation for adaptive control strategies in smart manufacturing.