Breast cancer is a significant health issue affecting women worldwide, and metastasis, the spread of cancer to other parts of the body, plays a crucial role in determining treatment and prognosis. Accurate detection of metastasis, particularly in the lymph nodes, is essential for effective treatment planning. Traditional diagnostic methods have limitations, and recent advancements in artificial intelligence (AI), machine learning (ML), and deep learning (DL) offer promising solutions for improving and supplementing diagnostic procedures. This article focuses on reviewing the predictive models in breast cancer metastasis using machine learning and artificial intelligence, emphasizing accuracy, comparison, advancements, and challenges. The article will discuss using prediction models based on clinical, pathological, genetic, or imagological features for breast cancer metastasis detection, validation techniques, and potential pitfalls or limitations. Additionally, the article will explore future directions and best practices to achieve high usability in real-world clinical settings.