The increasing prevalence of Alzheimer’s disease, particularly among women, highlights the need for the development of effective predictive models and preventative strategies to mitigate this growing public health concern. This research compares multiple deep learning approaches to test their usefulness in predicting and preventing Alzheimer’s disease in women. In this manuscript, researchers used and contrasted many designs, including RNNs and CNNs. The researcher’s results reveal that the CNN technique performs similarly to other models in terms of accuracy, precision, recall, and F1 score with an Area Under the Curve (AUC). The method described in the paper has several strengths, including high model Accuracy, Precision, Recall, and F1 scores were all 98.10%, with an AUC of 99.82% for early detection of Alzheimer’s in women, leveraging MRI and PET scans. It outperforms existing methods and provides automated diagnosis, aiding faster clinical decisions. The model’s use of large datasets (Kaggle, OASIS, ADNI) enhances its generalizability. However, it has weaknesses, such as reliance on high-quality data, potential overfitting, high computational demand, and challenges with clinical integration. Additionally, the complexity of interpreting deep learning models may limit their adoption in clinical settings. The CNN’s improved performance is due to its capacity to accurately identify spatial patterns in neuroimaging data, which is critical for early diagnosis and intervention. The manuscript highlights the potential of CNNs as a strong weapon in the battle against Alzheimer’s disease, opening up exciting paths for future research and treatments focused on women’s health.