<p>Breast cancer is the most prevalent cancer among women globally and ranks as a primary cause of cancer-related mortality, surpassed only by lung cancer. The condition arises from the atypical growth of cells in the breast ducts, which, if unrecognized, may result in serious health consequences. Conventional diagnostic techniques, including as mammography, ultrasound, and histology, have greatly aided in detection but frequently face limitations related to accuracy, reliability, and scalability. The advent of artificial intelligence (AI) and metaheuristic optimization algorithms has created novel prospects to transform breast cancer diagnosis. These methods, derived from biological, evolutionary, and swarm intelligence mechanisms, provide effective solutions for intricate optimization challenges, such as feature selection, image classification, and dimensionality reduction. This paper examines current developments in metaheuristic optimization algorithms and their application to breast cancer diagnosis, emphasizing three principal methodologies. The initial component is the Twin Convolutional Neural Network (TwinCNN) combined with the Binary Ebola Optimization Search Algorithm (BEOSA), a multimodal strategy that tackles the difficulties of categorizing breast cancer images obtained from several modalities. This approach attained cutting-edge classification accuracies of 97.7% for histology and 91.3% for mammography datasets. The second method involves Particle Swarm Optimization (PSO) integrated with Spatially Constrained Adaptively Regularized Kernel-Based Fuzzy C-Means (ScARKFCM) clustering, achieving a notable accuracy of 92.6% on the MIAS dataset, highlighting its efficacy in breast cancer image segmentation and classification. The enhanced Ant Colony Optimization method integrated with Residual Network-101 CNN illustrates the synergy between optimization and deep learning techniques</p>