Assisted reproductive technologies (ARTs) are valuable tools for couples struggling with infertility conditions and who desire to have their biological offspring, yet the reproducibility of ARTs stands at approximately thirty percent. To make appropriate treatment decisions, it is crucial to analyse the reproductive situation of women. For this purpose, gynaecologists and obstetricians observe women’s physical and mental health, their records of earlier treatments, and current ongoing medical prescriptions. However, there are often chances of ambiguity in understanding interpretations and the reproducibility rate of given data, as such interpretations are primarily based on and dependent on clinical staff reliance. In this scenario, artificial intelligence (AI) appears as a competent system for the ARTs process that can provide personalized and maximum data measurement accuracy through proper handling of the maternity process and analyse large, dynamic datasets with clear and diverse outcomes for an individual patient. But is an AI-generated dataset devoid of any mistakes and ambiguity? How do we ensure the correctness and unbiases of AI-provided data in reproductive medicine? These questions need to be explored before AI intervenes in the process of procreation. Thus, the present chapter is an attempt to expound and explore the way AI has shown the promising potential for personalization and optimization of the production of data, encompasses mensural cycle monitoring, drug dosing and selection, induction of oocyte maturation, and selection of embryos and gametes to enhance the safety and efficacy of the complete ART process to ensure the data correctness and minimization of mistake. The ethical dimension of the data generation and interpretation in terms of truth, justice, fairness and ambiguity will be taken into account.