Conventional semen analysis measures concentration, motility, and morphology but cannot fully characterize individual sperm function or determine a couple’s likelihood of conception. Artificial intelligence (AI) may quantify sperm morphology and movement at scale and support links with functional biomarkers and reproductive outcomes. To evaluate the promise and limitations of computational sperm phenotyping, distinguishing technical performance from biological validity and clinical utility. This focused narrative review synthesized selected guidance and publications on AI, computer vision, semen analysis, functional biomarkers, and reproductive outcomes. It was not a systematic review; we did not perform exhaustive searching, duplicate screening, or formal risk-of-bias assessment. Automated detection, tracking, morphology assessment, and kinematic analysis are technically feasible. Evidence linking AI-derived features to independent functional assays and prospective reproductive outcomes remains limited; small datasets, inconsistent labels, acquisition differences, leakage, and limited external validation constrain generalizability. Computational sperm phenotyping should complement but not replace standard semen analysis until biological and clinical validity are demonstrated. Progress requires multimodal datasets, transparent reporting, external validation, and evaluation against patient-important outcomes.