Alzheimer’s Disease (AD) is a neurodegenerative disorder that primarily affects the elderly, causing cognitive decline and memory loss. Traditional diagnostic methods, such as neuropsychological tests and cerebrospinal fluid analysis, are invasive and time-consuming. Neuroimaging techniques like MRI and PET provide valuable insights but require manual analysis by specialists. This study proposes a hybrid model combining EfficientNetB0, a deep learning architecture, with Convolutional Neural Networks (CNN) to automate AD detection in MRI scans. The model uses data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, which includes over 200 MRI scans and clinical information. Our results show that the hybrid model outperforms existing methods in accuracy and efficiency, detecting key AD pathology features such as amyloid beta plaques and neurofibrillary tangles. This work demonstrates the potential of AI-driven approaches for AD diagnosis, offering a more accessible, cost-effective solution for clinical settings with limited resources. Future research should explore multimodal integration and model interpretability. Keywords: Alzheimer's Disease, Deep Learning, MRI, EfficientNetB0, Convolutional Neural Networks.