This paper presents a systematic review and analysis of artificial intelligence (AI) based breast cancer diagnosis. The goal is to provide a helpful review of the literature for the breast cancer research community. The research papers are selected and shortlisted by conducting an extensive and exhaustive literature review through PRISMA. The artificial intelligence-based models, datasets, number of images, and problem type are reviewed with detailed performance analysis. The support vector machine (SVM) and convolutional neural network (CNN) are the most widely used AI models for breast cancer diagnosis. Digital database for screening mammography (DDSM) is the most used breast cancer research dataset. CNN and deep learning models require a more significant number of images for better performance in breast cancer research. It has been noticed from the review studies that the classification models such as SVM, extreme learning machine (ELM), CNN, and multi-layer perceptron (MLP) performed well as compared to the other existing machine and deep learning models.