Alzheimer’s disease (AD) is a neurodegenerative disease that causes irreversible damage to several brain regions, including the hippocampus causing impairment in cognition, function, and behaviour. Early diagnosis of the disease will reduce the suffering of the patients and their family members.This condition affects around 45 million individuals worldwide.Machine learning refers to an probabilistic technique that allow system to acquire knowledge from huge amount of data.In particular, the performance of different ML algorithms has been evaluated against their detection accuracy. The detection techniqueuses K-Means groups the unlabeled datasets into different clusters and Decision Treeis a tree- structured classifier ,where internal nodes represent the features of a datasets where branches represent the decision rules and each leaf node represents the outcome.Alzheimer disease is inherited in an auto- somal dominant pattern , which means one copy of an altered gene in each cell is sufficient to cause the disorder. In most cases, an affected person inherits the altered gene from one affected parent.To overcome this problem,this paper introduces an detection technique using machine learning Predictive genetic testing by collecting the genetic samples using k mean algorithm the datasetare stored Into different clustersand using decision tree algorithm the genetic samples are structured into relevant classiferWith the help of algorithms it is trained to detect the genetic pattern which has high pior risk of Alzheimer'sDisorder.