Alzheimer's disease, a progressive neurodegenerative disorder, necessitates early and accurate diagnosis to enable timely intervention and effective treatment planning. This study introduces an integrated machine learning framework for predicting the stages of Alzheimer's disease by leveraging multi-modal data, advanced preprocessing, sophisticated feature extraction, deep embedded clustering, and diverse ensemble learning methods. MRI, PET, genetic, and clinical data are utilized to construct a comprehensive dataset. The preprocessing phase includes denoising, bias correction, and normalization, ensuring high-quality input data. Feature extraction combines convolutional neural networks (CNNs) and traditional methods to capture high-level and intricate patterns. The core of the model employs deep embedded clustering to embed high-dimensional data into a lower-dimensional space, enhancing clustering accuracy. A diverse ensemble of base models, including Random Forests, Gradient Boosting Machines, Support Vector Machines, and neural networks, is trained on optimally selected features. Stacking and weighted voting based on cross-validation scores aggregate the predictions, ensuring robustness and reliability. The model addresses class imbalance through synthetic data generation and cost-sensitive learning. Interpretability is achieved using SHAP values and LIME, providing insights into the model's decision-making process. The proposed framework demonstrates superior performance in predicting Alzheimer's disease stages, offering a powerful tool for early diagnosis and clinical decision support.