Rural healthcare delivery in Bangladesh remains challenged by fragmented services, limited specialist access, delayed referrals, poor information sharing, and weak coordination between patients, community health workers, clinics, and hospitals. This study aimed to evaluate how a digital health platform can improve coordinated care delivery in rural Bangladesh and to predict the overall impact of such a platform using machine learning techniques. A dataset was developed based on rural healthcare service factors related to digital platform use, care coordination, patient access, communication, service efficiency, and perceived healthcare impact. The target variable was the Overall Impact of Digital Health Platform, classified into five categories: No impact, Low impact, Moderate impact, High impact, and Very high impact. Four machine learning models were applied using an 80/20 train-test split: Random Forest, XGBoost, LightGBM, and Artificial Neural Network using Scikit-Learn MLPClassifier. Model performance was evaluated through accuracy, precision, recall, F1-score, confusion matrix, ROC curve, precision-recall curve, learning curve, validation curve, calibration curve, and feature importance analysis. The Random Forest model achieved 82% accuracy, XGBoost achieved 94% accuracy, LightGBM achieved 98% accuracy, and the ANN model achieved the best performance with 99% accuracy, precision, recall, and weighted F1-score. The findings indicate that advanced machine learning models, particularly ANN and LightGBM, can effectively predict the impact of digital health platforms in rural healthcare systems. The results suggest that digital platform-based coordination can support improved healthcare accessibility, communication, decision-making, and service integration in rural Bangladesh. This study concludes that machine learning-supported digital health platforms may provide a practical approach for strengthening fragmented rural healthcare delivery systems.