Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that necessitates early and accurate diagnosis for effective intervention. Traditional diagnostic methods often fall short due to subjective interpretation and limited scalability. Deep neural networks (DNNs) offer a promising alternative for automated AD classification, yet the success of these models heavily depends on precise hyperparameter tuning. This paper presents a novel approach that leverages the Artificial Bee Colony (ABC) algorithm to optimize the hyperparameters of a DNN model tailored for Alzheimer’s disease classification using MRI images. The ABC algorithm, inspired by the foraging behavior of honeybees, effectively balances exploration and exploitation in the hyperparameter space, dynamically adjusting parameters such as learning rate, batch size, and layer configuration to achieve optimal performance. The Alzheimer MRI Pre-processed Dataset, which includes 6400 images categorized into four classes based on dementia severity (non-demented, very mild demented, mild demented, and moderate demented), was used to evaluate the proposed model. The ABC-tuned DNN achieved a classification accuracy of 92.3%, significantly outperforming models tuned with grid search (88.5%), random search (87.0%), and particle swarm optimization (PSO) (90.1%). Furthermore, the ABC algorithm enhanced the model’s computational efficiency, reducing convergence time to 70 epochs compared to 100 and 120 epochs for grid search and random search, respectively. Detailed performance analysis across the classes demonstrated high average precision (90.3%), recall (88.3%), and F1-score (89.2%), with especially strong results in distinguishing early stages of AD, such as non-demented and very mild demented. This robust classification performance highlights the ABC algorithm’s effectiveness in optimizing complex hyperparameter spaces in medical imaging models, contributing to more accurate and efficient AD classification. The ABC-tuned DNN model’s superior accuracy and convergence time make it a viable tool for clinical applications where timely and precise AD diagnosis is essential.