Alzheimer’s disease is one of the prominent neurodegenerative diseases that predominantly affects elderly people by damaging their brain cells. The patients suffering with Alzheimer’s lose their ability to perform day-to-day activities. It has become a topic of concern as the early symptoms are mild, which become relentless with time. Since there is no treatment available till date to cure this fatal disease, and the diagnosis is also done at the later stage, various machine learning techniques are being applied to predict the disease as early as possible. Machine learning has proved its effectiveness in various fields including the Medicare industry. The ability of these algorithms to accurately predict the disease makes it apt to foresee the presence of Alzheimer’s disease at its early stage. In this chapter, a comprehensive analysis of 3182 papers published, extracted from Scopus database is presented. The analysis is done on the basis of several criteria such as number of papers published per year in the field, the areas in which this research are done, the top authors in the field with maximum number of publications and maximum citation count, most used keywords, top countries contributing in the research. Co-occurrence studies are also done to analyze the relationship among various bibliometric elements.