Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. This research work finds that image feature extraction such as simple RGB Histogram Filter techniques on Alzheimer’s images dataset by implementing statistical learning. The Decision tree – J48 Classifier optimizer of ensemble category produced 51% of accuracy level, 0.510 of True Positive (TP) rate value, 0.163 of False Positive (FP) rate value, 0.507 of precision value, 0.510 value of recall value, 0.718 of receiver operating character (ROC) value and 0.478 of precision recall curve (PRC) value and it takes time consumption as 0.03 seconds to build a model which is produced as optimal results based on their performance compare with other models. The trees classifier of the J48 is best model for my proposed system.