Alzheimer’s disease, a memory-robbing brain condition, poses a significant worldwide health challenge. Early detection is essential for better patient outcomes. In this research, we explore the importance of Alzheimer’s detection and delve into the methods employed for accurate diagnosis. Detecting Alzheimer’s at an initial stage allows for personalized care and potential disease-modifying treatments. We implement a deep learning approach using Convolutional Neural Networks (CNNs) for Alzheimer’s detection. Specifically, we utilize two well-established architectures: InceptionResNetV2 and DenseNet121. These models excel at feature extraction from medical images, enabling us to capture complex patterns associated with Alzheimer’s pathology. InceptionResNetV2 combines ideas from Inception and ResNet, utilizing residual connections and inception modules. DenseNet121 uses a dense connectivity pattern between convolutional layers for feature reuse and potentially improving feature extraction capabilities compared to traditional architectures. It offers a good balance between performance and computational efficiency. Our experiments demonstrate promising outcomes: a 96.44% accuracy using InceptionResNetV2 and an impressive 97.64% accuracy with DenseNet121. The accuracy achieved by these models outperforms conventional CNN models.