Because of changing consumer habits, manufacturing processes are shifting from mass production to small-batch production, which is making machining more complex and increasing demands for precision and stability. Machine tools, and thus machining accuracy, are affected by factors such as temperature and cutting load. Existing online estimation techniques often require the installation of additional sensors at specific locations, an approach that has cost and reliability issues, thus limiting industry’s acceptance of these techniques. In practice, most manufacturers rely on offline detection methods, meaning that machining accuracy deviations can take some time to detect. In this study, we developed a technology for monitoring the status of machine tools; this technology, rather than requiring the installation of sensors, uses servo sensor signals to estimate accuracy, diagnose faults, and make recommendations regarding cutting depth parameters. The proposed method leverages a small number of experiments combined with extensive finite element analysis to construct a big data database, followed by the sensitivity and regression analyses of the generated database to produce an estimation model that evaluates machine tool conditions through servo feedback. The results showed that using linear regression to estimate the machine tool’s status achieves good accuracy and that linear regression is easier to implement for real-time compensation. Ultimately, these results can enhance production efficiency and machining accuracy, as well as prevent unforeseen breakdowns.