This research examines the effectiveness of combining psychometric tests with computational models for diagnosing neurodegenerative and vascular forms of dementia. The goal is to enhance diagnostic accuracy using tools such as the Hachinski Ischemic Score (HIS), Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA), in conjunction with machine learning technologies. The research framework integrates computational models to better analyze psychometric test data, aiming for early disease detection and more accurate differential diagnosis. The results suggest that this combined approach can reduce misdiagnosis rates and contribute to personalized patient treatment by creating an innovative diagnostic framework based on each patient’s unique profile.