Breast cancer is the major critical disease and suffered many people around the world. Recent
progress in Machine Learning (ML) and Deep Learning (DL) techniques has established as
valuable tools in breast cancer detection, enhancing both precision and effectiveness. This
paper contribute a comprehensive review of breast cancer prediction utilize ML algorithms and
DL model. ML is frequently employed in classifying breast cancer pattern due to exhibiting a
crucial feature detection from composite breast cancer datasets. ML techniques such as
Random Forests (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR) and Support
Vector Machines (SVM) are evaluate to classify and predict breast cancer based on various
features. DL techniques including, Artificial Neural Network (ANN), Generative Adversarial
Networks (GANs), Convolutional Neural Networks (CNNs), models have extracting attribute
from raw imaging data, autonomously learning and offering high accuracy and sensitivity. In
this analyses determine the most effective method for handling large datasets while maintaining
high prediction accuracy.