The assessment of painting surfaces at the microscale has been historically impeded by challenges related to limited resolution and accuracy in traditional methodologies. This study pioneers the utilization of non-contact 3D optical scanning technology, meticulously calibrated for nanoscale precision, to unravel the intricate features residing on painting surfaces. The initial phase employs the Point Diffraction Interferometer (PDI) for 3D optical scanning, incorporating meticulously optimized parameters tailored to nanoscale analysis. Subsequent phases involve the application of Phase Shifting Interferometry (PSI) and Holographic Interferometry (HI). PSI is employed to discern morphological alterations, while HI captures the nuanced color and optical characteristics embedded in the painting surfaces. To enhance the continuity of phase information, the Goldstein algorithm is introduced during phase stitching, fortifying the method’s robustness against discontinuities. Further refinement is achieved through the Iterative Closest Point (ICP) algorithm, orchestrating precise 3D data reconstruction. This process encompasses multi-view stereo matching and surface fitting, ensuring a meticulous representation of the painting surface geometry. The study meticulously presents detailed 3D optical scanning results, probing into the painting surface’s performance concerning nanoscale resolution, measurement accuracy, and color consistency. The unveiled findings showcase a remarkable minimum feature capture capability of 1.8 at nanoscale resolution. The quantitative assessment, encapsulated by a Root Mean Square Error (RMSE) ranging from 0.001 to 0.012 for 100 scanned data points, and a Standard Deviation (STD) oscillating between 0.0008 to 0.0018, attests to the method’s efficacy. This effectiveness is underscored by its capacity to deliver a thorough and intricate analysis of painting surface performance at the nanoscale.