In response to the evolving landscape of obstetric ultrasound assessments, this article introduces an innovative platform for the Automated Assessment of Obstetric Ultrasounds (US), taking advantage of the power of Machine Learning (ML) and web technologies, obtaining a diverse compilation of obstetric ultrasound image sets from various pregnancy cases and creating a robust dataset, that will serve as the foundation for training a sophisticated Machine Learning model integrated into the web platform. The primary objective is to equip the model to autonomously detect abnormal Nuchal Translucency (NT), but with the possibility of adding new models for extra parameters assessment. The proposed platform goes beyond traditional manual evaluations, presenting a more efficient and accurate approach to assess image patterns on 2D obstetric ultrasound images. This article outlines a visionary approach, merging medical imaging, web technologies, and machine learning to create a universally accessible platform for the Automated Assessment of Obstetric Ultrasounds. The potential impact on global healthcare highlights the significance of this innovative solution in advancing prenatal care.