Millions of people have already been impacted by Alzheimer’s disease, a chronic neurodegenerative illness that impairs cognitive function. Cases of the disease are predicted to rise dramatically every ten years. Alzheimer’s disease cannot be cured, but it can be managed and kept from getting worse with early diagnosis. For early detection, a number of techniques have been developed. Traditional home care was used at first, but as clinical research progressed, useful drugs were also used. Imaging scans and psychological therapies were also used to diagnose the illness. Computer-aided diagnostic systems (CADS) have emerged recently, utilizing cutting-edge technologies for detection such Machine learning (ML) and deep learning algorithms (DL) Algorithms. The effectiveness of both ML and DL models is assessed in this study, which also examines the methods employed over the previous three years. It covers the range of data and technology accessible for creating detection systems, giving researchers a starting point to suggest or use novel approaches. It also contrasts the results of ML and DL algorithms and points out areas that still need investigation in the literature. Web databases such as Google Scholar were searched for papers from 2021 to 2023 in this analysis, with an emphasis on MRI pictures and keywords such “Detection of Alzheimer’s disease.” Following a comprehensive assessment, 27 papers were chosen in total.