Dementia is a degenerative neurological condition that worsens with time and is characterized by a deterioration in cognitive abilities and difficulties in carrying out everyday activities. Timely identification of dementia is essential for prompt management and improved patient outcomes. Machine learning algorithms have shown potential in detecting patterns and signs linked to the development of dementia in recent times. This research introduces an innovative method for identifying dementia at an early stage by using machine learning algorithms on a range of datasets, such as medical records, neuroimaging scans, cognitive evaluations, and demographic information.The suggested technique encompasses the processes of data collection, preprocessing, feature selection, and the building of a machine learning model. Data preparation strategies address the issue of missing data, standardize features, and eliminate noise in order to provide machine learning models with high-quality input. The dataset is analyzed to identify and extract pertinent aspects that indicate the course of dementia. Multiple machine learning methods, including logistic regression, support vector machines, random forests, and deep learning architectures, are used to train and optimize the classification of people as either having dementia or being healthy.