The use of machine learning (ML) in biomarker analysis for predicting Down syndrome exemplifies an innovative strategy that enhances diagnostic accuracy and enables early detection. Recent studies demonstrate the effectiveness of ML algorithms in identifying genetic variations and expression patterns associated with Down syndrome by comparing genomic data from affected individuals and their typically developing peers. This review examines how ML and biomarker analysis improve prenatal screening for Down syndrome. Advancements show that integrating maternal serum markers, nuchal translucency measurements, and ultrasonographic images with algorithms, such as random forests and deep learning convolutional neural networks, raises detection rates to above 85% while keeping false positive rates low. Moreover, non-invasive prenatal testing with soft ultrasound markers has increased diagnostic sensitivity and specificity, marking a significant shift in prenatal care. The review highlights the importance of implementing robust screening protocols that utilize ultrasound biomarkers, along with developing personalized screening tools through advanced statistical methods. It also explores the potential of combining genetic and epigenetic biomarkers with ML to further improve diagnostic accuracy and understanding of Down syndrome pathophysiology. The findings stress the need for ongoing research to optimize algorithms, validate their effectiveness across diverse populations, and incorporate these cutting-edge approaches into routine clinical practice. Ultimately, blending advanced imaging techniques with ML shows promise for enhancing prenatal care outcomes and aiding informed decision-making for expectant parents.