<p>Infertility is clinically defined as the failure to achieve a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse. It is a multifactorial condition resulting from male, female, combined or unexplained factors, with male and female factors each contributing approximately 30–40%; combined factors accounting for around 20%; and unexplained infertility represents the remaining proportion. This balanced contribution highlights the importance of evaluating both partners simultaneously during infertility assessment. The traditional analysis of infertility in females may arise from various issues, like ovulation, oocyte maturation, fertilization competence, and the ability of a fertilized egg to undergo preimplantation development, implantation, and normal fetal growth. Male infertility is commonly associated with a decrease in semen quality, including decreased sperm counts or even the total deficiency of sperm in ejaculate, as well as abnormalities in sperm function and morphology due to impaired spermatogenesis or dysfunction of accessory reproductive glands. These symptoms may result from inherited or acquired causes, as well as inadequate integration of recent advances into clinical evaluation of recent advances in infertility diagnosis and management. In this context, three complementary technological pillars, like image analysis, gene analysis and electronic health records based data integration play a crucial role in infertility evaluation and management enabling a comprehensive evaluation of reproductive health conditions. This review critically examines and synthesizes evidence for infertility diagnosis using multimodal and data driven approaches in reproductive health. It discusses current diagnostic approaches, personalized medicine, preventive strategies, emerging technological advances and their broader public health perspectives.</p>