Ultrasound has become the primary modality for fetal central nervous system examination and diagnosing malformations. However, the effectiveness of fetal brain examinations remains highly operator-dependent. Deep learning, a key branch of artificial intelligence (AI), has demonstrated significant advantages in image recognition, proving particularly valuable in medical imaging. Consequently, several studies have proposed the use of deep learning models as tools for fetal brain ultrasound examinations. AI has achieved clinical applications in fetal brain ultrasonography, encompassing standard plane recognition, biometric measurements, structural identification, and malformation diagnosis. This review systematically analyzes the applications of AI in fetal brain ultrasound examination and discusses unmet clinical needs and future development.