Dementia is a progressive disease that impairs cognitive functions such as memory, thinking, and communication, affecting over 40 million individuals worldwide, according to the World Health Organization. Early diagnosis is essential for effective management and treatment. In this study, we present a Custom Convolutional Neural Network (CNN) model designed to analyze Magnetic Resonance Imaging (MRI) scans for dementia detection. The model classifies dementia into four stages: Non-demented, Very Mildly Demented, Mildly Demented, and Moderately Demented. Our system incorporates advanced preprocessing techniques, such as image resizing, Gaussian filtering for noise reduction, and normalization, to enhance image quality and ensure consistent inputs for the CNN. Performance evaluation of the Custom CNN was carried out using key metrics like accuracy, precision, recall, and the confusion matrix to measure classification performance. The Custom CNN achieved an accuracy of *98.18%*, demonstrating its effectiveness in accurately detecting dementia stages. The confusion matrix further confirmed the model’s high precision in classification, showing significant improvements over existing models. This result underscores the potential of the proposed system to support early diagnosis, leading to timely interventions and improved patient outcomes. Our Custom CNN model offers a substantial advancement in dementia detection, contributing to both research and clinical practice in addressing the global challenge of dementia.