Alzheimer’s disease (AD) is the most prevalent cause of dementia worldwide, yet early and accurate diagnosis remains a significant clinical challenge. This study systematically evaluates machine learning (ML) models for AD classification using a shared magnetic resonance imaging (MRI) dataset, focusing on both binary (e.g., healthy vs. demented) and multiclass (four-stage dementia) tasks. MRI images were preprocessed and deep features were extracted using a pretrained GoogLeNet architecture. Classification was performed using support vector machines for binary tasks and error-correcting output codes (ECOC) for multiclass tasks. Model performance was assessed using both hold-out and k-fold (5- and 10-fold) cross-validation (CV) strategies to ensure robust evaluation. Results indicate that CV yields substantially higher and more reliable accuracy than the hold-out method, with binary classification achieving up to 84% accuracy (10-fold CV) and multiclass classification reaching 87% (5-fold CV). The model demonstrated high specificity and precision, particularly for moderate-stage AD, but lower and less stable performance for early disease (very mild) cases. Learning curve analysis confirmed improved generalization with increased training data and minimal overfitting in well-validated models. However, our findings also reveal that high-performance metrics alone are insufficient to judge clinical utility. For example, a hold-out model distinguishing healthy from moderate Alzheimer’s achieved a seemingly impressive 99% test accuracy, but learning curves revealed severe overfitting and likely data leakage, indicating that such results would not generalize to new patients. In contrast, the healthy vs. mild Alzheimer’s task, with a more modest ~70% test accuracy, demonstrated well-behaved learning dynamics and genuine generalization. These results highlight that high reported accuracy is only clinically meaningful when supported by healthy training dynamics; otherwise, models risk underperforming in real-world clinical settings regardless of their reported metrics. We advocate for rigorous validation and learning curve analysis as prerequisites for any clinically actionable ML tool. These findings underscore the importance of transparent per-class performance reporting and robust validation to ensure that ML models can truly support early detection and staging of AD in clinical practice.