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This study develops a method to predict and compensate for thermal deformation in machine tools, focusing on selecting temperature-sensitive points, establishing a predictive model, and detecting temperature anomalies. Optimal sensing points were identified using feature ranking algorithms and Partial Dependence Plots, improving Z-axis deformation identification. The research also evaluated various machine learning models for Z-axis deformation prediction, notably optimizing Gaussian Process Regression for superior accuracy. Additionally, anomaly detection in temperature sensors was addressed using One-Dimensional Convolutional Neural Networks and Long Short-Term Memory Networks, enhancing system reliability and robustness. This approach offers a comprehensive solution for thermal deformation in machine tools, contributing to the field's precision and efficiency.