Alzheimer’s disease is a neurogenerative disorder that produces a particular global healthcare challenge. Early diagnosis is required for effective treatment. This research explores the potential of machine learning and deep learning techniques for predicting Alzheimer’s disease. The datasets encompass both numerical data and structural MRI scans, including cognitive test scores and genetic markers from individuals both with and without Alzheimer’s. A dataset containing various MRI scans, neuroimaging, and features was used to train and contemplate machine learning models. Numerous engineering and selection techniques were applied to enhance the model’s performance. Various classification algorithms were used in the implementation that predict Alzheimer’s disease. These models were strictly evaluated using different measures. The results indicate that ML models can effectively predict the disease based on a combination of neuroimaging features. This demonstrates the potential of ML in aiding early Alzheimer’s disease diagnosis, which is important for personalized treatment. Future work may involve refining and validating these models and exploring the integration of multi-modal data sources for even more robust predictions.