Alzheimer’s disease (AD) is a progressive brain disorder that affects memory, thinking ability, and daily functioning. Early detection of this disease is very important so that proper care and treatment can be provided at the right time. Traditional diagnosis methods mainly rely on clinical tests and brain scans, but they often fail to detect the disease at an early stage. In this research, a multimodal machine learning framework is proposed to improve the detection of Alzheimer’s disease. The framework combines three different types of data: MRI brain images, speech features, and cognitive assessment scores. Each modality provides unique information, MRI captures structural brain changes, speech reflects behavioral patterns, and cognitive scores represent clinical condition. The problem is formulated as a binary classification task to distinguish between cognitively normal individuals (Control) and patients with Alzheimer’s disease (AD). A 3D Convolutional Neural Network (3D CNN) is used to learn features from MRI data, while machine learning models are applied to speech and cognitive data. The outputs from all three modalities are combined using a model-level fusion technique called Out-of-Fold (OOF) stacking. The proposed model achieves an accuracy of 82.61% and an AUC score of 0.93, demonstrating strong performance in distinguishing between Control and Alzheimer’s subjects. The results show that combining multiple data sources improves detection compared to using a single modality. This study highlights the importance of multimodal learning for Alzheimer’s disease detection and provides a reliable and practical approach that can be useful in real-world healthcare applications.