A progressive neurodegenerative Alzheimer’s Disease (AD) shrinks (atrophy) the brain and kills brain cells. On a global scale, Finland and the United Kingdom have the highest prevalence of AD, with 54.7 and 42.7 cases per 100,000 inhabitants respectively. In India, there are 14.60 AD cases for every 100,000 people and it is ranked 137 ^th in the world and 13 ^th in India among the 50 causes of death. At the beginning of the disease, a person may forget things, or have trouble remembering recent conversations. As the disease progresses, a person may experience severe memory loss and difficulty performing everyday tasks. Consequently, the only way to predict AD at an early stage is to use treatments that prevent the disease from progressing. This paper provides an overview of the current approaches to AD diagnosis and detection, with a focus on the use of Biomarkers and discussed the advantages and disadvantages of Machine Learning (ML) / Deep Learning (DL) techniques in early detection of AD. It also reviews the collection of AD datasets and the pre-processing techniques used in the studies. Although DL methods gives the best outcome measures such as accuracy, precision, F-measures, recall and Area Under the Curve (AUC) still DL techniques have some limitations that can be overcome via Quantum Computing (QC). QC is currently emerging new technology based on quantum theory where qubits are used to store the data instead of bits. It accomplishes 1000 times faster and also solves the complex problem within a fraction of time than the classical computers.