Breast cancer is a leading cause of cancer-related mortality worldwide, emphasizing the need for accurate and efficient diagnostic tools. Traditional methods, while effective, face limitations such as false positives, high interobserver variability, and challenges with high-dimensional data. Swarm intelligence (SI), a subset of artificial intelligence inspired by the collective behavior of natural systems, has emerged as a transformative approach in addressing these challenges. SI algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC) have demonstrated their efficacy in predictive modeling, feature selection, and medical image analysis. This review explores the applications of SI in breast cancer diagnosis, highlighting its contributions to data pre-processing, tumour segmentation, and decision support systems. Additionally, the study examines the challenges of computational complexity, scalability, and ethical considerations, while proposing future research directions including hybrid SI systems and applications in personalized medicine. Swarm intelligence offers a robust, scalable, and adaptable solution for breast cancer diagnostics, with the potential to revolutionize clinical workflows and improve patient outcomes.