Breast cancer is the second most common cause of cancer-related death in women and one of the deadliest diseases. Malignant, carcinogenic tumors that develop from breast cells are the first signs of breast cancer. Periodic clinical check-ups help with early diagnosis and significantly improve survival chances. The clinical procedure that leads a significant challenge to researchers and scientists is the detection of breast cancer. Machine learning algorithms such as neural networks (NN) have recently become widespread for classifying cancer. Early identification plays a crucial role in enhancing treatment outcomes and survival rates. The study aims to find a model to help for predicting cancer early in the patient’s life. This paper has studied and compared machine learning (ML) methods such as support vector machines (SVM), K-nearest neighbors, Random Forest, Decision trees, Naive Bayes, Logistic Regression, Artificial Neural Networks, and Deep Neural Networks. Further, we have employed a well-known breast cancer dataset to validate the above machine-learning techniques. The experimental outcomes signify that the deep neural network achieves 98.8% accuracy compared to ANN (97%) and random forest (94.7%) techniques.