Early prediction of the Alzheimer disease (AD) is important because it prevents detection of the disease in its early stages that are best addressed by clinical intervention and management of the disease in its early stages, and since the cognitive symptoms overlap and are non-linear in nature. The current research indicates a hybrid classifier paradigm of earlystage prediction of Alzheimer disease by secondary data of cognitive assessment by voting. The methodology is quantitative and experimental in nature and includes data preprocessing, feature selection and combination of several base classifiers, these include Decision Tree, Support Vector Machine, k-Nearest Neighbour and Logistic Regression. Voting strategies used to combine predictions of individual classifiers include majority, weighted and soft voting to increase the accuracy of the classification. The findings of the experiment show that the voting-based hybrid model and weighted voting strategy, in particular, are more accurate, precise, and recall better and might be more robust than individual classifiers. The analysis of feature contribution leads to a focus on memory and executive functioning as the most critical areas of cognition, and the analysis of confusion matrixes proves that there is no significant misclassification of memory and cognitive stages of close closeness. Generally, the results confirm the usefulness of ensemble learning in the initial stage of the Alzheimer disease and indicate that the suggested hybrid model can be consistent and clinically useful decision-making model in cognitive assessment-based diagnosis.