Dementia is a worldwide concern and early discovery of dementia is substantial for the administration of mental illness and healthy living. The data acquired from Magnetic resonance imaging (MRI), Positron Emission Tomography (PET) and Computed Tomography (CT) are used in identifying dementia. Albeit numerous non-AI and AI techniques have been implemented to understand the reasons behind dementia there is very little success to predict at an early stage. Considering the self-determination medicine, a new healthcare model that could be delivered by 5G, there is a pressing need to diagnose dementia patients in early stages using deep learning techniques and that can constantly improve assessment and diagnostic tools for distinguishing people with normal brain aging from those who will develop mild cognitive impairment. We will conduct state-of-the-art research to identify various deep learning algorithms and approaches that can be used for the diagnosis of dementia and aid in predicting at an early stage. We will develop and evaluate an intelligent algorithm that will predict and distinguish people with normal brain aging from those with mild cognitive impairment on a constant basis, which will be a good fit to the future healthcare model that will be delivered by 5G.