The classification of neurodegenerative diseases like Parkinson's and Alzheimer's is a good way of classifying diseases because it aids early diagnosis. The paper discusses a prediction of brain age by utilizing regression algorithms using the structural MRI scan. Later, classification with the aid of images was achieved using CNN. A number of regression models compared were the following: linear, ridge, lasso, random forest, and support vector regressions. Result: It resulted in demonstrating ridge and lasso regression returned very low mean absolute errors and high R-squared values, so those were pretty useful for brain age prediction. Big discrepancies between the predicted and actual brain ages likely reflected neurodegenerative conditions. In such cases with an important deviation, classification based on the CNN used was performed to discriminate Parkinson's, Alzheimer's, and normal aging. This approach reached very high accuracy, precision, recall, and F1-scores, as confirmed by annotated MRI scans and corresponding disease labels. Key steps involved MRI data preprocessing, feature extraction, and training a machine learning-based model. Performance testing ensured that the framework could accurately predict brain age and distinguish healthy aging from neurodegeneration. This model resulted in a great classification accuracy of 99.69%. Such a method provides a highly promising framework for early detection of neurodegenerative diseases that, in turn, allows for early interventions with positive patient outcomes. Future research might be concentrated on model tuning, generalizing toward unseen data, and the study of interpretation techniques to increase classification decision explanations. This methodology clearly shows that the integration of medical imaging with highly advanced machine learning techniques opens pathways for successful impactful clinical applications.