Breast cancer remains a significant global health challenge affecting millions of people worldwide. Early detection is crucial for improving treatment outcomes and survival rates. With the rapid advancement of technology, artificial intelligence (AI) has emerged as a transformative tool in medical diagnostics, particularly in breast cancer detection. This review examines how state-of-the-art machine learning (ML) and deep learning (DL) methodologies have revolutionized breast cancer diagnostics. Techniques such as convolutional neural network (CNN), ensemble learning, transfer learning, explainable AI, and federated learning (FL) have been analyzed for their contributions to addressing multifaceted challenges in medical image analysis. Each approach was evaluated for its capacity to enhance detection accuracy, interpretability, and scalability in real-world applications. The integration of these methodologies offers a comprehensive solution by combining the strengths of individual techniques. For instance, CNN excels in feature extraction, ensemble learning enhances robustness, and transfer learning influences the efficiency of pre-trained models. Concurrently, explainable AI improves transparency, and FL ensures data privacy while enabling collaborative research. Collectively, these innovations facilitate early diagnosis and pave the way for personalized treatment strategies, ultimately improving patient outcomes. By synthesizing recent findings, this study aims to provide insights into the current state of AI in breast cancer diagnostics, identify research gaps, and encourage future developments. Through the effective integration of AI technologies, healthcare systems can optimize resource allocation and deliver improved care to those affected by breast cancer.