Alzheimer’s Disease (AD) is the utmost deadly neurological illness, it slowdowns the thinking capacity that can substantially impair human’s to perform their regular works or tasks. Timely finding of Alzheimer’s Disease can help in therapy and avoid brain tissue damage. Estimates indicate that AD may rank third as a cause of death for older people, after heart disease and cancer. Common clinical tests are time consuming, fail to recognize early signs and lack of diagnosis sensitivity and specificity. Current examine finds three stages of Alzheimer’s disease: Preclinical Alzheimer’s Disease, Mild Cognitive Impairment (MCI) due to Alzheimer’s Disease, and dementia due to Alzheimer’s Disease. In the last two stages, symptoms are present, but to varying degrees. Identifying persons at risk of developing Alzheimer’s Disease is crucial for assessing treatment options. In terms of predicting and diagnosing Alzheimer’s Disease, Machine Learning Algorithms have showed potential. In this paper, I proposed exploratory data analysis and how different machine learning algorithms like Logistic Regression (LR), Random Forest Classifier (RFC), Decision Tree Classifier (DTC), Support Vector Machine (SVM), and Neural Networks (NN) are accustomed to forecast Alzheimer’s Disease and avoid likelihood of risk for the existing Data Set, and I developed a procedure to find the best algorithm with the highest accuracy for better prediction and treatment.