Preeclampsia remains a leading cause of maternal and perinatal morbidity and mortality globally. Accurate and early prediction is crucial for timely intervention and improved outcomes. Machine learning (ML) has emerged as a promising approach for identifying individuals at high risk of developing preeclampsia by leveraging complex patterns within diverse datasets. However, translating promising ML research models into effective, reliable, and scalable clinical deployment presents significant challenges. This article reviews the current landscape of machine learning applications in preeclampsia prediction, focusing on identified deployment patterns and key predictive features. We synthesize findings from recent literature, discussing commonly employed ML algorithms, the types of data and features utilized (including maternal characteristics, biomarkers, and clinical history), and the reported predictive performance. Crucially, we examine the challenges and considerations related to the practical implementation of these models within healthcare systems, including data quality, model interpretability, integration into clinical workflows, and the necessity of robust MLOps practices. This review highlights the critical need to address deployment-related aspects to ensure that ML models for preeclampsia prediction can move beyond research settings and achieve real-world clinical impact, ultimately contributing to improved maternal health outcomes.