Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disorder that is the leading cause of dementia. It is characterized by the accumulation of abnormal protein deposits in the brain, disrupting normal brain cell function. Symptoms develop slowly and worsen over time, including memory loss, difficulty with language and problem-solving, confusion, and changes in mood and personality. Researchers have proposed and implemented a hybrid framework that combines the Gray wolf optimization algorithm (GWO) and multiple discrete wavelets transform (DWTs) algorithms to achieve early detection using a support vector machine (SVM) and convolutional neural network (CNN). This framework involves several essential steps, including data acquisition, preprocessing, and image-to-signal transformation; feature extraction using four discrete wavelet transform systems (demy, semy, bior1, db8); feature selection through a Gray wolf optimization algorithm (GWO), and SVM-based classification and convolutional neural network (CNN). These steps are critical for developing accurate and reliable machine and deep-learning models for Alzheimer’s disease detection. The study’s results demonstrate the effectiveness of the proposed system, achieving an average accuracy of 94.5% using a support vector machine and 95.4% using a convolutional neural network in detecting Alzheimer’s disease. The integration of machine learning and deep learning algorithms such as SVM, CNN, and Gray wolf optimization for feature selection significantly contributes to the model’s accuracy. This research emphasizes the importance of early detection of Alzheimer’s disease and showcases the machine's potential and deep learning techniques using brain magnetic resonance images (MRI) to accomplish this objective