Background: Preeclampsia remains a leading cause of maternal and perinatal morbidity and mortality worldwide, affecting 2–8% of pregnancies globally. Early prediction is critical for implementing prophylactic interventions, yet traditional risk models demonstrate limited predictive accuracy. Multimodal artificial intelligence (AI) approaches integrating clinical, biochemical, and ultrasound data offer a promising avenue for improving early detection. Objective: This systematic review synthesizes evidence on multimodal AI models for early prediction of preeclampsia, evaluating model architectures, data integration strategies, predictive performance, and barriers to clinical translation. Methods: Following PRISMA 2020 guidelines, a comprehensive search of PubMed, Scopus, Embase, IEEE Xplore, and Web of Science conducted through July 2025. Studies were eligible if they developed or validated AI models integrating at least two data modalities (clinical, biochemical, ultrasound, or other imaging) for preeclampsia prediction. Risk of bias assessed using PROBAST. Results: From 2,457 identified records, 18 studies met inclusion criteria. Multimodal AI models demonstrated area under the curve (AUC) values ranging from 0.78 to 0.98, consistently outperforming unimodal approaches and traditional logistic regression. The highest-performing models integrated maternal characteristics, mean arterial pressure, placental growth factor (PlGF), and uterine artery Doppler indices. Deep learning architectures, particularly convolutional neural networks for ultrasound analysis and ensemble methods for structured clinical data, showed superior performance. However, only 22.2% of studies performed external validation, and substantial heterogeneity in predictor definitions, outcome ascertainment, and validation strategies limited comparability. SHAP analysis revealed that PlGF, mean uterine artery pulsatility index, and maternal history contributed most strongly to predictions. Conclusions: Multimodal AI demonstrates significant potential for early preeclampsia prediction, with performance exceeding traditional screening. However, limited external validation, inconsistent reporting, and underutilization of ultrasound data constrain clinical translation. Prospective multicenter validation and standardized reporting frameworks are essential before routine implementation.