<p>Alzheimer’s disease poses a substantial challenge due to the desperate need for enhancing early detection methods for this debilitating neurodegenerative condition. Present diagnostic methodologies frequently depend on subjective clinical evaluations and invasive procedures, resulting in delays in diagnosis and commencement of treatment. Therefore, this paper proposes a structured novel Squeeze Excitation Light Channel Network with a Random Lyrebird algorithm for Alzheimer’s sisease classification comprising six fundamental stages, each integral to the overall process. Initially, the focus is on data collection to gather pertinent medical images. Subsequently, in the data preparation stage, rigorous preprocessing techniques are applied to ensure data quality and consistency. In the third stage, augmentation techniques are then employed to enrich the dataset to bolster model robustness and enhance generalization capabilities. The fourth stage employs cross-validation to systematically evaluate and refine the classification model across multiple data subsets. Following this, the fifth stage utilizes a modified variation autoencoder for feature extraction, optimizing the representation of critical Alzheimer’s disease-related features. Finally, the sixth stage enables effective Alzheimer’s disease detection by applying a novel proposed algorithm. It aims to advance Alzheimer’s disease diagnosis and prognosis, leveraging cutting-edge techniques to enhance accuracy and reliability in clinical settings. From the analysis, the proposed model demonstrates superior efficiency with an accuracy rate of 98.45%. These results underscore the proposed model’s efficiency in accurately diagnosing Alzheimer’s disease compared to existing methods, highlighting its potential for clinical application and patient care.</p>