A comparison of ML Models for the early identification of Alzheimer’s disease (AD) using MRI scan data is presented in this work. The 3199 photos in the dataset, which represent the normal and Alzheimer’s brain classes, are split into 2720 training images and 479 test images. Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and the VGG16 model were among the models that were assessed. CNN models’ generalization was enhanced by the use of regularization strategies like weight decay and dropout. Additionally, grid search cross-validation was utilized for hyperparameter adjustment in order to maximize the SVM’s performance. With an accuracy of 98%, the VGG16 model outperformed CNN with regularization at 95% and SVM with hyperparameter tuning at 96%, according to the data. These results demonstrate the superiority of the VGG16 model in identifying Alzheimer’s disease, highlighting the significance of hyperparameter tweaking and regularization in creating successful machine learning models for medical diagnostics. In subsequent research, k-fold cross-validation and meta-heuristic optimization techniques will be used to enhance model resilience and prediction performance. Addressing the tiny size of the dataset and looking into interpretability techniques for deep learning models are also necessary to improve clinical applicability.