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Alzheimer's disease (AD), a progressive dementia, is the neurodegenerative disorder that worsens memory and mental capabilities mostly in aged people. Currently, clinical and psychometric assessments are being used to diagnose the disease in patients. In clinical procedures, 30 Magnetic Resonance Image qualitative parameters are analyzed to identify the abnormality in brain shape, volume, texture, and cortical thickness. This paper presents a robust approach for categorizing 30 MR images into multiple stages of AD using hybrid features viz., Gray Level Cooccurrence Matrix (GLCM), 30 Scale and rotation Invariant Feature Transform (30 SIFT), Histogram of Oriented Gradients-Three Orthogonal Planes (HOG-TOP), and Complete Local Binary Pattern of Sign and Magnitude-Three Orthogonal Planes (CLBPSM-TOP). The proposed algorithm is validated using Open Access Series of Imaging Studies (OASIS) datasets to classify the subjects into AD, Mild Cognitive Impairment (MCI), and Cognitive Normal (CN) categories using various classifiers. Moreover, this approach is also evaluated and compared with the state-of the-art approaches. 87 .84% diagnosis accuracy is achieved with Ensemble classifier using hybrid features to diagnose the severity of AD. This approach also outperforms the majority of these techniques in key parameters, namely, accuracy, precision, recalI, and F1-score. [ABSTRACT FROM AUTHOR]