Breast cancer continues to be one of the leading diseases affecting and claiming the lives of women globally. The need for detection of breast cancer in its early stages is very important for enabling treatment to be carried out and for increasing number of survivors. This research helps to detect breast cancer at an early stage by employing advanced deep learning techniques. This research can save lives by making people aware of the disease. This approach implements a deep neural network (DNN) model to predict breast cancer using healthcare data. The model sequential network design begins with data cleaning, which is standardization using Dense, Batch Normalization, and Dropout layers. After data preprocessing, deep neural network is applied to find the accuracy of the model. This model achieves remarkable precision, which indicates the ability of deep learning toward improving breast cancer diagnostic systems. Results from the study show that optimized network topologies facilitate better Network Performance, thus enhancing accuracy and reducing false-positive rates to clinically relevant levels.