Infertility is a major global health challenge, affecting 10–15% of couples of reproductive age. With the advancement of emerging technologies, particularly artificial intelligence (AI), new perspectives have been introduced for improving the diagnosis and treatment of infertility. This study was conducted as a systematic review following PRISMA 2020 guidelines, including studies published between 2015 and 2025 across major scientific databases. Findings demonstrate that AI has a wide range of applications in infertility management, such as predicting the success of assisted reproductive treatments (ART) using clinical variables (age, BMI, AMH, sperm parameters, and follicle count), analyzing time-lapse incubator images to evaluate embryo quality and predict implantation potential, optimizing sperm selection and detecting abnormalities through machine vision and deep learning algorithms, and forecasting pregnancy complications. These applications highlight the transformative potential of AI in enhancing diagnostic accuracy, reducing human errors, and enabling personalized treatment strategies. Nevertheless, challenges remain regarding algorithm transparency, the need for extensive clinical validation, and ethical considerations. Thus, the safe and effective integration of AI in infertility care requires the establishment of robust interdisciplinary frameworks and regulatory guidelines.