This work developed a cell damage recognition method based on single-cell arrays using an artificial intelligence tool. The method uses micropatterns (single-cell micropatches and microwells) to isolate each cell in an ordered array to minimize cell overlapping and to maintain cell contours. After exposure to a therapeutic drug (e.g., doxorubicin), a large number of single cells are monitored, and the cell damage levels are determined with both morphology and intensity changes in reactive oxygen species recorded under fluorescence microscopy. The convolutional neural network model is trained by the time-series cancer cell images before and after low and high concentrations of drug exposure. The trained model can identify cancer cell status (live/dead) and classify damage levels (major/moderate/minor) with high accuracy. The single-cell pattern allows cells physically segmented at the single-cell level, which not only eliminates the need for computational cell segmentation but also reduces background noise and neighboring interference, which highly enhances the accuracy of analysis via image recognition. The single-cell array accelerates the computational analysis for toxicity with a trained AI model, which can be used to predict cell damage response for screening potential anticancer drugs.