Alzheimer’s disease (AD) is a debilitating brain disorder that gradually impairs memory, thinking abilities, and behavior, predominantly affecting the elderly population. Early detection is crucial for managing symptoms and improving the patient’s quality of life, as there is currently no known cure for the disease. This study explores the potential of machine learning (ML) and deep learning (DL) algorithms in detecting Alzheimer’s at an early stage. By analyzing cognitive assessments, medical records, and neuroimaging data, the study aims to develop a predictive model to identify patterns and indicators of the disease. The outcomes of this study revolutionize Alzheimer’s diagnosis and management, leading to personalized interventions and treatments. Ultimately, this research contributes to a deeper understanding of Alzheimer’s and offers hope for improved care strategies and patient outcomes. The developed CNN model enhances image categorization and quality, outperforming previous research with a prediction accuracy of up to 96.4%. The subjective and objective analysis shows that our proposed model outperforms the existing state-of-the-art comparative models.