<p>Thermally induced errors are among the most significant factors influencing machining accuracy. They can represent as much as 70% of the total machining error. As a key part of computer numerical control machine tools, the spindle system is one of the most important factors leading to machine tools' thermal deformation errors. Therefore, the machining accuracy depends on the precision degree of the spindle system. Since the spindle thermal errors are difficult to eliminate completely during the design process, error compensation becomes the most cost-effective alternative. Precise, efficient, and robust predictive modeling of thermal errors is a crucial step to achieve effective machine tool accuracy improvement through error compensation. This paper presents a simplified and unified predictive modeling method for spindle thermal error for real-time error compensation. The proposed method relies on several temperature and spindle thermal drift measurements, design of experiments, neural networks modeling, and diverse analysis tools to supply an economical selection of suitable thermally sensitive points and modeling parameters to achieve accurate and robust thermal error modeling and prediction. The proposed method offers the advantage of a simple application procedure, limited modeling time, and uncertainty. Various tests conducted on a CNC turning center confirmed the suggested method practicality and effectiveness and demonstrated that the resulting predictive model led to a reduction in thermal errors of 80%—90%, proving their accuracy and efficiency in predicting time-varying thermally induced errors under varied conditions.</p>