Breast cancer continues to be a major global health issue, making early and accurate identification crucial for improving patient survival rates. This study investigates various methods for breast cancer classification, comparing conventional machine learning models with sophisticated deep learning techniques. The research investigates various feature extraction techniques, including the Gray Level Co-occurrence Matrix (GLCM), Histogram of Oriented Gradients (HOG), and Local Binary Patterns (LBP), to improve the detection of pertinent patterns in medical imaging. The gathered attributes are further examined utilizing machine learning classifiers, such as Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), to assess their effectiveness in categorizing breast cancer patients. This study investigates deep learning models, such as Convolutional Neural Networks (CNNs), VGG16, and InceptionV3, utilizing conventional techniques that can autonomously extract hierarchical features from medical images. The comparison of machine learning with deep learning methodologies underscores deep learning’s superiority in managing intricate patterns, diminishing reliance on human feature extraction, and enhancing classification effectiveness. Subsequent research highlights the significance of feature selection techniques in enhancing model effectiveness and accuracy. Experimental results demonstrate that deep learning models markedly exceed conventional machine learning techniques in classification accuracy, robustness, and generalization capacities. CNN-based architectures, like VGG16 and InceptionV3, exhibit exceptional effectiveness in identifying complex patterns in breast tissue images. These findings highlight the increasing importance of deep learning in medical diagnostics, emphasizing its potential to create reliable and automated techniques for breast cancer diagnosis. This research underscores the imperative of utilizing deep learning techniques to improve breast cancer detection. Future developments must concentrate on enhancing model architectures, diversifying datasets, and incorporating explainability methods to ensure transparent and clinically relevant AI-driven diagnostic solutions.