Due to its complicated and multifaceted character, Alzheimer's disease (AD), a progressive neurodegenerative condition, poses a huge challenge in the medical field. For appropriate intervention and treatment planning, accurate and early disease stage prediction is essential. This study uses Magnetic Resonance Imaging (MRI) data to demonstrate a novel machine learning method called Fuzzy Distributed Ensemble Learning (Fuzzy-DEL) for predicting the phases of Alzheimer's disease. Creating an ensemble classification model that divides Alzheimer's disease into four stages—no dementia, mild dementia, moderate dementia, and severe dementia—is the goal of this study. The suggested Fuzzy-DEL method uses a Binary Relevance framework for multi-label classification, Fuzzy C-Means clustering, Recursive Feature Elimination (RFE) for feature selection, and a Random Forest classifier. The creation and use of the Fuzzy-DEL technique, which handles the high dimensionality of MRI data and the multi-class nature of Alzheimer's disease staging, is the primary contribution of this study. By collecting more intricate patterns in the data, this method enables a more nuanced treatment of high-dimensional data, potentially enhancing the model's performance. Given the rising frequency of the condition and the complexity of its diagnosis and treatment, there is an urgent need for such cutting-edge machine learning approaches in the field of Alzheimer's research. Preliminary findings show that the Fuzzy-DEL methodology works better than conventional machine learning techniques in terms of accuracy and robustness, highlighting its potential as an important tool for predicting the stage of Alzheimer's disease