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Abstract Cellular biomechanics plays a critical role in cancer metastasis and tumor progression. Existing studies on cancer cell biomechanics are mostly conducted in flat 2D conditions, where cells’ behavior can differ considerably from those in 3D physiological environments. Despite great advances in developing 3D in vitro models, probing cellular elasticity in 3D conditions remains a major challenge for existing technologies. In this work, optical Brillouin microscopy is utilized to longitudinally acquire mechanical images of growing cancerous spheroids over the period of 8 days. The dense mechanical mapping from Brillouin microscopy enables us to extract spatially resolved and temporally evolving mechanical features that were previously inaccessible. Using an established machine learning algorithm, it is demonstrated that incorporating these extracted mechanical features significantly improves the classification accuracy of cancer cells, from 74% to 95%. Building on this finding, a deep learning pipeline capable of accurately differentiating cancerous spheroids from normal ones solely using Brillouin images have been developed, suggesting the mechanical features of cancer cells can potentially serve as a new biomarker in cancer classification and detection.