Alzheimer’s disease (AD) presents a pressing challenge as a progressive neurodegenerative disorder significantly impacting memory and cognitive functions. Timely diagnosis and intervention are imperative for enhancing the prognosis and quality of life for individuals affected by AD. However, existing methods for AD detection often exhibit limitations such as invasiveness, high costs, or subjectivity. The paper consists of innovative approach for AD detection utilizing structural magnetic resonance imaging (MRI) data and leveraging field-programmable gate array (FPGA) technology. Our research focuses on the development and simulation of a hybrid deep learning model that integrates the dual-tree complex wavelet transform (DTCWT) and convolutional neural network (CNN) architectures to effectively extract and classify features from MRI images. Furthermore, we implement this model on an FPGA platform to achieve hardware acceleration and operational efficiency. Through rigorous evaluation using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, we compare the performance of our approach with existing methods. The results demonstrate that the proposed approach achieves high accuracy and low latency in AD detection, using CNN and ANN models. This research underscores the potential of utilizing FPGA technology for accelerating AD detection processes, showcasing its viability for clinical applications and highlighting its significance in advancing the field of neuroimaging-based diagnosis.