<p>The growing demand for high-precision multiaxis machining in aerospace, energy, and advanced manufacturing sectors has heightened the need for rotary indexing systems that offer both structural rigidity and intelligent health management capabilities. However, existing studies typically address structural design, accuracy validation, or fault diagnosis independently, resulting in limited integration between mechanical performance evaluation and data-driven intelligence. This study proposes an integrated evaluation and diagnostic framework for a dual-axis roller-cam rotary indexing table, combining finite element modeling, experimental modal analysis, precision accuracy verification, and principal component analysis (PCA)-based intelligent fault diagnosis within a unified platform. The theoretical foundation of the proposed approach is based on structural dynamics theory and multivariate statistical feature extraction, enabling the coupling of physical vibration behavior with probabilistic classification models. Experimental results demonstrate indexing accuracy within 11.6″ to 19.8″ and repeatability below 2.2″ across all axes after compensation. A PCA–Gaussian mixture model (GMM)-based diagnostic system achieved a classification accuracy of 93.5%, with an area under the ROC curve exceeding 0.92, outperforming conventional FFT- and wavelet-based methods. This study presents one of the first integrated structural–diagnostic frameworks specifically designed for dual-axis roller-cam rotary systems, effectively bridging the long-standing gap between mechanical accuracy validation and intelligent condition monitoring. The proposed methodology establishes a new paradigm by combining physics-based structural verification with data-driven fault intelligence, thereby enabling scalable predictive maintenance and cyber–physical integration in next-generation intelligent machining systems.</p>