Alzheimer’s disease is a neurological disorder that is progressive and primarily affects memory and other cognitive functions. According to the studies conducted by World Health Organization, or WHO, “over 55 million individuals worldwide suffer from dementia and that around 10 million new cases are recorded each year”. Furthermore, accounting for 60–70% of dementia cases, Alzheimer’s disease is the most frequent cause of the condition. To effectively treat Alzheimer’s disease (AD), a precise diagnosis is essential, especially in the early stages when preventive actions can be performed to lessen the irreversible impairment of the brain. The traditional methods of diagnosing Alzheimer’s disease, such as clinical evaluations, medical history analysis, cognitive assessments, and brain imaging, have limitations, including delayed detection of symptoms, subjective interpretation, and invasive procedures. The recent discoveries in neuroimaging, biomarkers, deep learning, and machine learning algorithms have created novel opportunities for early detection and improved diagnostic accuracy. The main objective of the paper is to review some of the important literature on Alzheimer’s disease and gain insight into how deep learning and machine learning can aid in the timely identification of the condition.