Women exhibit marked physiological transformations in pregnancy, mandating regular and holistic assessment. Maternal and fetal vitality is governed by a spectrum of clinical, demographic, and lifestyle factors throughout this critical period. The existing maternal health monitoring techniques lack precision in assessing pregnancy-related risks, often leading to late interventions and adverse outcomes. Accurate and timely risk prediction is crucial to avoid miscarriages. This research proposes a deep learning framework for personalized pregnancy risk prediction using the NFHS-5 dataset, and class imbalance is addressed through a hybrid NearMiss-SMOTE approach. Fifty-one primary features are selected via the LASSO to refine the dataset and enhance model interpretability and efficiency. The framework integrates a multimodal model (NFHS-5, fetal plane images, and EHG time series) along with two core architectures. ResRNN-Net further combines Bi-LSTM, CNNs, and attention mechanisms to capture sequential dependencies. MultiScaleFusion-Net leverages GRU and multiscale convolutions for effective feature extraction. Additionally, TabNet and MLP models are explored to compare interpretability and computational efficiency. SHAP and Grad-CAM are used to ensure transparency and explainability, offering both feature importance and visual explanations of predictions. The proposed models are trained using 5-fold stratified cross-validation and evaluated with metrics including accuracy, precision, recall, F1-score, and ROC–AUC. The results demonstrate that MultiScaleFusion-Net balances accuracy and computational efficiency, making it suitable for real-time clinical deployment, while ResRNN-Net achieves higher precision at a slight computational cost. Performance comparisons with baseline machine learning models confirm the superiority of deep learning approaches, achieving over 80% accuracy in pregnancy complication prediction.