This chapter analyzes the transformative role of Explainable AI (XAI) in healthcare, with a particular emphasis on reproductive health services. It provides an overview of the stages of XAI, the tools used, and evaluation protocols, supplemented by relevant case studies. These case studies demonstrate the significant potential of XAI in areas such as male fertility prediction, embryo selection, maternal health risk assessment, PCOS diagnosis, and endometriosis management, offering promising avenues to enhance reproductive outcomes and address fertility challenges effectively. The findings highlight the critical need for early detection of fertility issues in both men and women through XAI techniques as a proactive measure to mitigate the rising infertility rates. Besides, IVF success rate prediction is identified as a crucial application of XAI, particularly in addressing the financial and emotional stress associated with the high costs and time demands of IVF treatments. These advancements could encourage broader adoption of AI-driven approaches in reproductive medicine. Additionally, the chapter presents an in-depth exploration of XAI’s broader applications in healthcare, focusing on common conditions like cancer, cardiovascular diseases, and Alzheimer’s. By promoting transparency and informed decision-making, XAI is poised to drive innovation, improve clinical outcomes, and raise the overall standard of care within the medical industry.