With the growing demand for manufacturing precision, efficiently identifying geometric errors in five-axis machine tools has become essential for achieving high-accuracy machining. This paper presents a self-calibration method utilizing a tower-shaped workpiece, enabling the simultaneous identification of geometric errors in the rotary axis (C-axis), linear axes, and the workpiece itself. The approach involves designing a five-tier stepped tower-shaped workpiece and incorporating a three-dimensional volumetric error model for the linear axes, which discretizes linear axis errors at grid nodes within a 3D space. An overdetermined system of equations is constructed from multi-angle probing data, and the error parameters are estimated using the least squares method. Experimental results successfully identify four position-independent and six position-dependent geometric errors of the C-axis, alongside the geometric errors of the linear axes and the workpiece. The findings demonstrate the method′s high stability in identifying C-axis errors under repeated clamping, confirming its potential for long-term monitoring. To further assess accuracy, a comparative experiment using a disk-shaped reference workpiece was conducted, revealing an average consistency of 89.8% in error identification results. A Monte Carlo-based uncertainty analysis confirms the method′s robustness and reliability in the presence of measurement system errors and environmental disturbances. Importantly, the proposed method does not rely on high-end measurement equipment or complex path planning. It supports repeatable clamping and automated measurement, offering advantages such as ease of operation, low cost, and strong adaptability. This makes it a practical and efficient solution for high-precision geometric error identification in five-axis machine tools.