For embryo selection, AI algorithms analyze embryo images to assess their quality and potential for successful implantation. These algorithms use: (i) time-lapse imaging: AI monitors embryo development over time, evaluating factors such as morphology, cell division patterns, and dynamics; (ii) morphokinetic data: algorithms predict the likelihood of an embryo developing into a viable pregnancy by identifying subtle features not visible to the human eye. AI also helps improve sperm selection by: (i) sperm motility analysis, identifying the most motile and morphologically normal sperm; (ii) DNA integrity checks, assessing DNA fragmentation levels in sperm to select the healthiest candidates. Machine learning models analyze multiple data points to predict the success rate of IVF, including patient history (age, hormonal levels, lifestyle factors), clinical data (ovarian reserve markers, endometrial receptivity), as well as embryo quality metrics. AI can optimize ovarian stimulation protocols by personalizing medication dosages based on patient-specific responses, and predicting ovarian response to stimulation, reducing the risk of ovarian hyperstimulation syndrome (OHSS). AI-driven automation streamlines processes in ART labs, including monitoring and controlling incubator conditions, standardizing embryo grading to minimize human error, and managing cryopreservation protocols. AI leverages large datasets from clinics and research studies to identify trends and factors influencing ART success and improve treatment protocols by recognizing patterns in patient and embryo data.