Background/Objectives: Artificial intelligence (AI)-assisted automatic fetal measurements during ultrasound evaluation have become a major subject of interest. Most publications include systems developed and tested for a single anatomical component. In routine clinical practice, multiple biometric parameters are required for fetal weight estimation. Our objective is to assess the studies that incorporated three or more standard plane fetal automatic measurements with the help of AI systems in the second and third trimesters. Methods: We performed a systematic review of the published research focusing on automated AI systems analyzing at least three standard fetal biometric parameters or their corresponding standard ultrasound planes for fetal biometric, gestational-age, or fetal-growth assessment. The literature screening had to meet the predefined inclusion and exclusion criteria. Results: Nineteen studies, comprising more than 730,000 ultrasound images, met the inclusion criteria, and eighteen studies compared automated measurements with manual reference measurements. Overall, the included studies reported favourable performance for automated assessment of biparietal diameter, head circumference, abdominal circumference, and femur length, although substantial heterogeneity was observed in study design, AI architecture, validation methodology, and reported performance metrics. Conclusions: Automated AI systems show promising performance for assessing multiple standard fetal biometric parameters and may improve measurement consistency and examination efficiency. However, substantial heterogeneity in datasets, model architectures, and outcome reporting restricts direct comparison and the certainty of the evidence. Standardized reporting and prospective multicentre evaluation across gestational ages (GA), ultrasound systems, and operators are needed before these systems can be recommended for routine clinical use.