The research presents a deep learning approach for detecting complications like preterm birth and endometriosis using machine learning techniques using two datasets: a dataset of ultrasound images which consists of various parts of the fetus and mother, and another dataset of histopathological image samples which are microscopic photos of the endometrium tissue present in the uterus lining. These images capture maternal-fetal development for monitoring parts of fetus and maternal cervix at different weeks of the prenatal period and provide valuable insights. The model uses convolutional neural networks (CNNs) architecture based on Densely Connected Convolutional Networks and Efficient Nets to analyze these images, classifying ultrasound images into features such as Fetal Abdomen, Fetal Brain, Fetal Femur, Fetal Thorax and Maternal Cervix. By training on the Maternal fetal ultrasound dataset of 12400 images, the model achieves a 94.40% accuracy, assisting healthcare professionals in early interventions and improved neonatal outcomes. The second dataset of microscopic image samples contains photos of endometrial specimens stained with hematoxylin and eosin (H&E) for better understanding. The accuracy achieved by the model trained on this dataset is 82.40%. Endometriosis is a painful condition which affects growth of endometrium tissue present at the uterine lining. Ideally, laparoscopy is required to check for endometriosis, but for cases of endometriosis during pregnancy, it was found that analysis of endometrium tissue samples can also be done to detect endometriosis. The approach used in this study is based on transfer learning, which involves fine-tuning pre-trained models like Dense Net and Efficient Net, to classify endometrium tissue samples.