Alzheimer’s disease (AD) poses a significant global health challenge, making early and efficient detection essential for timely intervention. While three-dimensional (3D) T1-weighted MRI is widely used for AD detection, its clinical applicability is often hindered by high acquisition costs, prolonged scan durations, complex preprocessing pipelines, and the heavy computational requirements of volumetric deep learning models. Additionally, 3D models typically demand large annotated datasets and extended training time—constraints that limit their deployment in real-world, resource-limited settings. In contrast, two-dimensional (2D) T1-weighted MRI slices provide a cost-effective and computationally efficient alternative. These slices retain localized pathological features vital for diagnosis and can be leveraged effectively through modern representation learning. In practical settings, 3D scans may be missing or degraded, while 2D slices remain accessible and informative. This study introduces a hybrid deep learning-based framework for AD classification using 2D T1-weighted MRI slices from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We introduce a series of methodological innovations, including advanced preprocessing techniques, weighted majority voting with confidence scores, the integration of statistical and pixel-based features, and a hybrid architecture that fuses pre-trained deep learning models with classical machine learning classifiers. Our best-performing model, the Weighted Augmented Hybrid Network (WAHNet), achieves an accuracy of 80.1% in three-class classification (AD, Mild Cognitive Impairment, and Normal Control), representing an 8% improvement over baseline methods, including those based on 3D CNNs (64%) and 3D pixel-based features (71.9%). The proposed framework demonstrates that 2D imaging, when combined with hybrid learning, can deliver diagnostic performance comparable to 3D methods while improving scalability and clinical usability.