Machine learning with deep learning is a cutting-edge technology that has over time become a principal trend in the industry. It is omnipresent and heavily used within many applications. Machine learning, pushed by deep learning, is the backbone of organizations related to finance, medical science, and security. Deep learning the branch of machine learning, performs brilliant analysis on large and complex data like images and unstructured medical records. Together, these are used in discovering patterns from medical data sources, and power especially brings good capabilities to predict diseases that enable advancements in early diagnosis and personalized treatment. This paper discusses various machine learning techniques used in diagnosing Alzheimer’s disease: it provides an account of how efficient these techniques are on different datasets. Techniques such as CNN, RF, and modified RF applied to ADNI and OASIS datasets showed varied effectiveness. The present paper contributes to filling a gap of knowledge or research on how better systems could be designed to assist in decision-making for medical purposes.