One of the most common cancers in the world is Breast Cancer, with over two million diagnoses every year, which are often caused by proliferation of breast cells leading to lumps or tumors. The high incidence of this disease in developed countries can partly be explained by lifestyle, genetic factors, and access to diagnostic technology. Improved survival rates are associated with early detection and effective treatment; however, outcomes are unsatisfied in many low- and middle-income countries. That is why early detection is crucial for an adequate response to this disease. Computational approaches can be essential to improving diagnostic accuracy and support for healthcare professionals by seeing which algorithm is the most effective in early detection. Breast cancers can be accurately identified by doctors using computers and algorithms. In this study, we used metaheuristic algorithms which includes Tabu Search, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), and Simulated Annealing to compare their F1-score, accuracy, precision and area under curve (AUC). The Wisconsin breast cancer dataset consisting of benign and malignant cases is used to test these algorithms and the performance is measured in matrices such as F1-score, accuracy, AUC, and precision. Particle swarm optimization (PSO) showed the least accuracy (54), compared to other algorithms. In conclusion, Tabu search and genetic algorithms showed the highest accuracy at 98% and 94%, respectively. These two algorithms are effective to identify important features in the data. By integrating these approaches into the diagnostic process, this study summarizes the potential of these methods. The findings are useful in research aimed to improve early breast cancer detection.