<p>Error compensation is a cost-effective way to improve machine accuracy without making significant changes to the configurations of the machine tools. The efficiency of the compensation process strongly depends on the accuracy of the model used to predict the errors that need to be compensated. This paper presents an improved, robust quasi-static error predictive modeling approach for real-time error compensation in CNC machine tools. The proposed approach combines a generalized geometrical error model derived using the homogeneous transformation matrix, a systematic selection procedure for temperature-sensitive variables, and neural network modeling as well as a measurement system for displacement errors and temperatures using a laser interferometer, displacement non-contact sensors, and temperature sensors to achieve effective machine tool accuracy improvement through error compensation. The proposed approach offers the advantage of a straightforward and efficient application. Various tests conducted on a CNC machining center validated its practicality and effectiveness, demonstrating that the resulting predictive model reduced errors by more than 85%. This confirms the modeling procedure accuracy and efficiency in predicting time-dependent errors under diverse conditions.</p>