Breast Cancer (BC) remains a leading cause of morbidity and mortality among women globally, accounting for 30% of all new cancer cases (with approximately 44,000 women dying), according to recent American Cancer Society reports. Therefore, accurate BC screening, diagnosis, and classification are crucial for timely interventions and improved patient outcomes. The main goal of this paper is to provide a comprehensive review of the latest advancements in BC detection, focusing on diagnostic BC imaging, Artificial Intelligence (AI) driven analysis, and health disparity considerations. We first examine diverse imaging techniques such as Mammography, Ultrasound, and Dynamic Contrast-Enhanced Magnetic Resonance Imaging, and provide an overview of their pros and cons. Then, we provided an intensive review of the State-of-the-Art (SOTA) literature on the role of AI in BC classification and segmentation. Lastly, we examined the role of AI in BC health disparities. A key contribution of this work lies in its integrative approach, consolidating insights from multiple research areas, imaging methods, AI-driven methodologies, and health disparities in a single resource. This paper evaluates the effectiveness of modern AI-based tools in enhancing diagnostic accuracy and discusses their potential to address biases in BC diagnosis, thus promoting equitable healthcare access. By integrating clinical, technical, and equity perspectives, this review aims to inform real-world decision-making, supporting the development of bias-aware AI tools, guiding equitable screening policy, and enhancing clinical practice in breast cancer care. Additionally, our critical analysis and discussion of recent SOTA highlights the strengths, limitations, and knowledge gaps for future directions of AI roles in BC. In total, these findings and future venue suggestions serve as a practical reference for researchers, clinicians, and policymakers, underscoring the need for interdisciplinary collaboration to harness AI's full potential in BC diagnosis and reduce global health disparities.