This work aims to develop a model using Convolutional Neural Network (CNN) for classifying fetal ultrasound images to diagnose fetal brain abnormalities. Ultrasound can identify the majority of major structural fetal abnormalities. By using TensorFlow and Keras frameworks, it preprocess the dataset and construct a CNN architecture which has convolutional and fully connected layers, and optimizes the model’s accuracy with exceptional precision. Through continuous training and validation, this model gains the ability to correctly identify foetal pictures as normal or abnormal. This helps in early detection of anomalies and intervention for expectant mothers, thereby improving outcomes for both mothers and babies.