AI is being used to enhance embryo selection and optimize culture conditions, leading to improved success rates. Machine learning helps identify patterns in embryo development and patient responses, enabling more personalized treatment plans. Indeed, notable improvements were observed in the accuracy of diagnosing and predicting successful outcomes in fertility treatments. AI-driven models provided more precise forecasts of the optimal timing for clinical interventions such as egg retrieval and embryo transfer, which are critical to the success of ART cycles. In vitro gametogenesis offers several potential advantages, including: enabling reproduction for individuals with impaired fertility due to lack of functional sperm or eggs, allowing same-sex couples to have genetically related offspring, providing greater control over genetic selection through embryo screening, and potentially reducing the physical burden on women by eliminating the need for ovarian stimulation during egg retrieval; however, this technology is still in early stages and raises ethical concerns regarding genetic manipulation and potential misuse. IVG has shown promise in animal models, including creating offspring with biological contributions from same-sex parents.