Cephalopelvic Disproportion (CPD) is a condition where the fetal head or body is too large to pass through the mother’s pelvis, affecting about 1 in 250 pregnancies. This complication often leads to emergency Cesarean sections, especially in rural areas with limited emergency care facilities. Traditional prediction methods, based primarily on maternal anthropometric measurements, achieve a limited accuracy of about 24%, prompting a need for more reliable approaches. This study proposes an innovative method that integrates maternal anthropometry, pelvic bone shape classification via MRI, and fetal head circumference to enhance CPD prediction accuracy. Using a dataset of 500 DICOM images, pelvic shapes were categorized into Gynecoid (lower CPD risk) and non-Gynecoid (higher CPD risk) types. Non-Gynecoid cases underwent further analysis of fetal head circumference using ultrasound. Advanced machine learning and deep learning techniques, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Convolutional Neural Networks (CNN), were employed to classify the images, achieving accuracy rates of up to 97.62%. These findings suggest that combining multiple diagnostic parameters provides a more robust pre-labor CPD prediction tool, potentially reducing emergency interventions and improving maternal and fetal outcomes, particularly in settings with limited access to advanced medical care. Future work will focus on expanding the dataset and refining the algorithms to further enhance prediction accuracy. This approach paves the way for early, non-invasive intervention strategies, offering significant benefits for both mothers and their newborns, particularly in settings with limited access to advanced medical care.