Fetal health analysis is the modern component of prenatal care, which assesses the health and developmental status of an unborn child. Due to the need for prompt and accurate diagnosis in order to detect developmental issues that could impact the growth, long-term health, or survival of the fetus, this procedure has traditionally relied on skilled radiologists interpreting ultrasound pictures through manual means, a cumbersome and subjective method prone to variation and errors. These difficulties highlight the need for scalable and trustworthy automated diagnostic systems, particularly in limited resource environments.This paper explores the use of the Vision Transformer (ViT) model in classifying fetal health from ultrasound images. Unlike traditional Convolutional Neural Networks (CNNs), ViT utilizes self-attention techniques for high-dimensional information processing, leading to the detection of global contextual patterns. It is therefore appropriate for medical imaging applications as it processes images patch-wise, detecting long-range dependencies and fine-grained anomalies in an image. The ViT model was trained on 12,000 annotated ultrasound images representing various levels of developmental problems that were classified as healthy, suspected, or abnormal. The proposed model of ViT offers reliable, automatic, and efficient categorization, thus combating the limitations of human diagnostics. It reduces the number of hours required to peruse large datasets by negating inter-radiologist variability and enhancing diagnostic acuity. This method would be feasible for expert-level diagnostics even at locations with limited resources, where the impact is especially acute in healthcare settings. The results indicate that ViT is more accurate and reliable than CNNs, hence a game-changer for prenatal care processes.