There was a huge population of people suffering from mental diseases all across the world. The primary need was to conduct experiments with an adequate examination of the brain disease. Dementia has been the primary factor of brain disease. Trauma or merely becoming older could cause a decline in cognitive abilities including learning, remembering, and problem-solving. "Alzheimer's Disease (AD)" proved to be the most widespread type of dementia. AD has been linked to cognitive impairment in 60-80% of cases. There is currently no effective therapy for this condition. Even though the early diagnosis of AD proved to be very beneficial for those living with this condition. As a branch of "Artificial Intelligence (AI)", "Machine Learning (ML)" approaches use a wide range of statistical and optimizing procedures to enable machines to acquire knowledge from large and complicated data sources. As a result, researchers all across the globe are increasingly turning to ML to identify the earliest stages of AD through "Computerized AD Detection (CADD)". The research attempts to provide a review of the several methods used to diagnose AD based on neurology and cognitive examination of the brain. In an attempt to further accurately identify AD mostly prior, it has been recommended here to investigate and analyze the evaluation of continuing techniques. While certain methods are effective at identifying AD, these have been evaluated using data from a wide variety of neuroimaging modalities, which collectively pose valid concerns about the reliability of the data used. Several studies using neuroimaging modalities for AD detection might assist from this reviewed article.