Radiotherapy with cell cycle-specific anticancer agents has become an important option in the control of both primary tumors and metastases. Here, we used image analysis algorithms that enable quick segmentation and tracking to describe a radiobiological approach for the optimized selection of cell cycle-targeting anticancer drugs for radiotherapy. We confirmed cell cycle-synchronization using human cervical cancer HeLa cells expressing a fluorescent ubiquitination-based cell cycle indicator (FUCCI) as a cell cycle-monitoring probe. Cells synchronized in the G1 and G2 phases were irradiated with X rays at 0.5–2 Gy. Each cell was identified using Cellpose, a deep learning-based algorithm for cellular segmentation, and the velocity and direction of migration were analyzed using the TrackMate plugin in Fiji ImageJ. G1 phase synchronized cells showed a dose-dependent decrease in velocity after irradiation, while G2 cells tended to increase their velocity. The migration pattern of all cells appeared to be a random walk model, regardless of the exposure dose. In addition, we used cisplatin to arrest the cell cycle. HeLa-FUCCI cells arrested at the G2 phase via cisplatin treatment showed enhanced cell migration after X-ray exposure. These results indicated that anticancer agents that arrest the cell cycle of cancer cells in a specific phase may enhance cell migration after radiotherapy. Our approach, using cellular segmentation and tracking algorithms, could enhance the radiobiological assessment of cell cycle-specific migration after irradiation to aid in optimizing radiotherapy using cell cycle-targeting agents.