Fetal growth assessment is a critical component of prenatal care, providing valuable insights into fetal development and overall pregnancy health. Traditional methods, relying on manual measurements of fetal biometry parameters such as Biparietal Diameter (BPD), Head Circumference (HC), Abdominal Circumference (AC), and Femur Length (FL), though effective, are time-intensive and prone to variability among practitioners. We have developed an AI algorithm based on deep learning techniques called CyNet which gives fetal biometry parameters with accurate dimensions with a segmentation dice score of 0.90. The integration of Artificial Intelligence (AI) in this field represents a significant advancement by automating these processes, enhancing both accuracy and efficiency. AI-driven algorithms streamline and simplify the extraction and analysis of fetal growth data from ultrasound images, ensuring consistent, reliable fetal biometry measurements. This white paper explores how AI-powered fetal growth assessment can enhance prenatal care, driving improved outcomes for both patients and healthcare providers.