The increase in risk awareness enables patients to take preventive action even before irreversible brain damage occurs. A correct diagnosis of Alzheimer’s disease (AD) is crucial to patient treatment, particularly in the early stages of the disease despite the fact that numerous recent studies have employed. The majority of machine detection techniques rely on congenital observations to diagnose AD using computers. Early diagnosis of AD is possible; however, it cannot be predicted because prediction is only useful up until the point at which the disease starts to show symptoms. Deep learning (DL) is now a widely used method for AD early diagnosis. Here, we examine how DL can assist researchers in making early diagnoses of AD and provide a quick overview of some of the key works in the field. A widespread and well-known neurodegenerative illness that impairs cognition is Alzheimer’s disease (AD). The ‘nervous system’ problem has drawn the greatest interest in the field of medicine. In spite of this thorough investigation, there is no method or remedy to halt or reduce its spread. However, there are numerous solutions (both pharmaceutical and non-medication alternatives) that can help treat AD symptoms at different stages and improve the patient’s quality of life. Patients must receive the right care at each stage of the disease as it progresses. Therefore, it may be beneficial to identify and categorise AD phases before beginning symptom treatment. About 20 years ago, there was a significant acceleration in the field of machine learning (ML) advancement. This work, which use ML techniques, focuses on early Identification of AD. We performed extensive testing to identify AD in the ‘Alzheimer’s Disease Neuroimaging Initiative’ (ADNI) dataset. The three categories that were to be created out of the dataset were AD, ‘cognitive normal’ (CN), and ‘late mild cognitive impairment’ (LMCI). The ensemble model of ‘logistic regression’ (LR), ‘random forest’ (RF), and ‘gradient boost’ (GB) is presented in this study as logistic random forest boosting (LRFB). There is currently a lot of interest in using machine learning to discover metabolic disorders that impact a huge number of people worldwide, such as diabetes and Alzheimer’s. Every year, their incidence rates rise at a startling rate. When it comes to Alzheimer’s, changes caused by neurodegenerative disorders impact the brain. An increasing number of people, their families, and the healthcare system will be affected by disorders that impair memory and functioning as our population ages. There will be significant social, financial, and economic repercussions from these. Alzheimer’s disease is unpredictable when it is first developing. When AD is treated early on, it is more successful and results in less minor harm than when it is treated later. A key concern in computer-aided detection (CAD) is the classification of brain diseases. The two main causes of death are brain tumours and Alzheimer’s disease (AD). Positron emission tomography (PET), computed tomography (CT), and magnetic resonance imaging (MRI) scans are used in the study of various disorders. Need specialised knowledge to comprehend the modality. The illness most commonly affects the elderly and has a potentially devastating latter phases. The outcome can be ascertained by computing the score from the Mini-Mental State Examination, after which the MRI brain scan is performed. In addition, a variety of classification techniques, including deep learning and machine learning, are helpful in the diagnosis of MRI scans. They do, however, have some accuracy-related restrictions. This research presents several novel pre-processing techniques that greatly enhance these MRI images’ classification ability. It also shortened the amount of time needed to train the model using different prior learning algorithms. The Alzheimer’s Disease Neurological Initiative (ADNI) provided a dataset that was transformed from a 4D format to a 2D one. Techniques for histogram equalisation, selective clipping, and greyscale image conversion were employed to prepare the pictures. Following pre-processing, three learning techniques for the classification of AD were developed. That is convolution neural networks (CNNs), XGBoost, and random forest.