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Atomic force microscopy (AFM) is routinely used as a metrological tool among diverse scientific and engineering disciplines. A typical AFM, however, is intrinsically limited by low throughput and is inoperable under extreme conditions. Thus, this work attempts to provide an alternative with a conventional optical microscope (OM) by training a deep learning model to predict surface topography from surface OM images. The feasibility of our novel methodology is shown with germanium-on-nothing (GON) samples, which are self-assembled structures that undergo surface and sub-surface morphological transformations upon high-temperature annealing. Their transformed surface topographies are predicted based on OM-AFM correlation of 3 different surfaces, bearing an error of about 15% with 1.72\(\times\) resolution upscale from OM to AFM. The OM-based approach brings about significant improvement in topography measurement throughput (equivalent to OM acquisition rate, up to 200 frames per second) and area (\(\sim\)1 mm2). Furthermore, this method is operable even under extreme environments when an in-situ measurement is impossible. Based on such competence, we also demonstrate the model’s simultaneous application in further specimen analysis, namely surface morphological classification and simulation of dynamic surfaces’ transformation.