Alzheimer's disease (AD) is an advanced neurodegenerative disorder designate by cognitive decline and memory loss, posing significant challenges for early diagnosis and intervention. Recently, Artificial Intelligence (AI) techniques have emerged as promising tools for AD prediction. In this paper, we compare two prominent learning models i.e. Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) for AD prediction. CNN model leverages its ability to inevitably learn hierarchical features from raw imaging data, employing multiple convolutional and pooling layers to extract spatial patterns indicative of AD progression. Meanwhile, the SVM model utilizes a linear or polynomial kernel to construct an optimal decision boundary between AD-positive and AD-negative cases based on handcrafted features derived from the imaging data. When evaluated, the CNN model attained an accuracy of 87.41% with a precision of 88.41%, recall of 87.41%, and F1 score of 87.57%, whereas the SVM model demonstrated outstanding performance, with a training accuracy of 100% and testing accuracy of 98.83%, precision of 99.21%, recall of 98.44%, and an F1 score of 98.83%.