Alzheimer disease is a chronic neurodegenerative disorder and the primary cause of dementia among the population, which has a huge burden to the patients, their caregivers and the health care system. Timely intervention is necessary to reduce disease progression, facilitate timely intervention and improve the quality of life. But the traditional forms of diagnostic are frequently costly and non-available especially in resource-deficient environments. The research paper proposes an interpretable and cost-efficient machine-learning model that can be used to identify the presence of Alzheimer disease at its early stages based on clinical and demographic metrics based on the Open Access Series of Imaging Studies cross-sectional dataset, which contains 436 participants. The data consists of seven numeric and two categorical variables, whereas the Clinical Dementia Rating was changed into two categories namely demented and non-demented. An extensive preprocessing pipeline was used, which entailed missing value imputation, categorical encoding and elimination of irrelevant variables, as well as class balancing with the Synthetic Minority Oversampling Technique. A number of machine learning models were tested, which comprise Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, and Extreme Gradient Boosting. The results show that the highest accuracy of 92% was attained using the model implemented by the ensemble and the tree, with the most accuracy being returned by the Random Forest and the ensemble model. Random Forest, too, had a sensitivity of 95%, whereas Gradient Boosting and Extreme Gradient Boosting had the highest area under the receiver operating characteristic curve of 98%. The models were implemented as a lightweight web application on the Flask framework, which can make real-time predictions and color coded. The system illustrates the possibility of combining interpretable machine learning with web technologies to make it possible to conduct easy and effective early screening of Alzheimer disease under resource-limited healthcare conditions.