According to the Alzheimer Report 2022, dementia is a serious global health issue that now affects over 55 million people globally and accounts for around 10 million new cases each year. Dementia is the sixth most common cause of death among chronic illnesses, which highlights the urgent need for sophisticated diagnostic equipment. With 60–70% of cases, Alzheimer’s disease (AD) is the most common kind. It is followed by Parkinson’s-related dementia, vascular dementia, Lewy body dementia, and frontotemporal dementia. Forgetfulness (affecting 80–90% of patients) and item misplacement (60–80%) are common symptoms. The diagnosis of dementia may benefit greatly from recent developments in artificial intelligence. Dementia phases have been successfully detected using machine learning techniques such as ensemble learning, gradient boosting, and support vector machines (SVM). Similarly, deep learning models like CNNs, CNN VGGNet, RNNs, and LSTM networks have found considerable success. With impressive accuracies of 96.64% and 98.39% on MRI-based pictures from the OASIS dataset, a novel method called ADCPBOBM demonstrated its resilience over a range of dataset sizes.