<b>The Role of Artificial Intelligence in Infertility Management:</b>
<b>Background:</b> Infertility affects an estimated 10–15% of reproductive-aged couples worldwide. Diagnostic evaluation still leans on operator-dependent imaging, subjective embryo grading, and treatment protocols built on population averages rather than individual patient trajectories. Artificial intelligence (AI) has moved from a research curiosity to a genuine adjunct in several corners of reproductive medicine over the past decade.<b>Objective:</b> To provide a narrative overview of how AI is being applied across the infertility care pathway diagnostic evaluation, assisted reproductive technology (ART), and outcome prediction and to outline where the evidence is strong, where it is thin, and what stands between current research and routine clinical use.<b>Methods:</b> This is a narrative, not a systematic, review. Literature was identified through targeted searches of PubMed, Scopus, Web of Science, and Google Scholar (2015–2025) using combinations of “artificial intelligence,” “machine learning,” “deep learning,” “infertility,” “IVF,” and “embryo selection.” Sources were selected for relevance and synthesized qualitatively. No PRISMA screening process, dual-reviewer selection, or fixed inclusion/exclusion protocol was applied, and no specific count of included studies is claimed.<b>Results:</b> The strongest evidence sits in embryo selection, where deep learning models trained on time-lapse imaging have shown externally validated performance approaching or exceeding embryologist agreement. Evidence for AI in ovarian reserve prediction, semen analysis, and broader IVF outcome prediction is more exploratory active research areas, but not yet supported by the kind of prospective, multicenter validation that would justify routine clinical deployment.<b>Conclusion:</b> AI shows real, if uneven, promise across infertility care. Embryo assessment and outcome prediction are furthest along; ovarian reserve modeling and male-factor diagnostics are earlier-stage but active. Clinical adoption depends on prospective validation, transparency about model limitations, and keeping a clinician in the loop rather than replacing one.<br>