Background and Objective: Alzheimer’s disease (AD) is a challenging neurodegenerative disorder to diagnose, necessitating innovative solutions for early detection and classification. Traditional diagnostic methods often lack sensitivity or scalability, highlighting the need for advanced approaches. This study proposes a dual-model framework integrating an Artificial Neural Network (ANN) and a Convolutional Neural Network (CNN) to enhance diagnostic accuracy. Methods: The framework combines two AI models. The ANN was trained on clinical data from 1,200 patients, incorporating 31 demographic, symptomatic, and behavioral features, to assess Alzheimer’s risk. The CNN analyzed 4,876 Magnetic Resonance Imaging (MRI) images to confirm the diagnosis and classify the disease into four stages: mild demented, moderate demented, very mild demented, and non-demented. Grad-CAM visualizations enhanced interpretability, offering clinically relevant insights. Results: The ANN model achieved an accuracy of 87.08% in assessing Alzheimer’s risk, while the CNN model excelled with a 97% accuracy in disease staging. Grad-CAM visualizations highlighted critical regions in the MRI images, enhancing the transparency and reliability of the diagnostic process. The results demonstrate the complementary strengths of both models in providing a comprehensive diagnostic solution. Conclusion: The integrated ANN-CNN framework shows promise in revolutionizing Alzheimer’s diagnostics by combining clinical and imaging data for accurate detection. While limited by MRI availability and variability in clinical data, the framework underscores AI's potential in advancing neurodegenerative disease diagnosis. Future directions include integrating wearable technology and lightweight CNNs to improve scalability, accessibility, and early intervention.