<p>Alzheimer’s disease (AZD) is a central nervous system ailment that worsens with time and causes brain atrophy and cognitive impairment. To improve quality of life and decrease its progression, early detection is crucial. EEG signals, which have a reputation for mirroring neural activity, present a rich yet intricate source of information because of their spontaneous and fast changes. To tackle this challenge, the study suggests a new framework called Multi-Channel Quantum Cascaded Visual Attention Neural Network (MCQ-CVANNet) for precise and interpretable AZD detection based on EEG signals. The pipeline starts with noise-resistant preprocessing via Trainable Joint Bilateral Filters (TJBF), followed by feature extraction via a hybrid Discrete Cosine–Krawtchouk–Tchebichef Transform (DCKTT) to extract discriminative spatial–temporal patterns. The classification phase utilizes MCQ-CVANNet, a quantum convolution, and a visual attention-based network that pays attention to significant features between EEG channels. The experimental results demonstrate the usefulness of the model, with F1 score of 99.93%,&#xa0;precision of 99.94%, specificity of 99.92% recall of 99.95%, and accuracy of 99.96%. Contrary to most black-box methods, proposed system prioritizes interpretability and robustness to allow clinicians to comprehend and have faith in the predictions made by the model. The suggested approach attained very high accuracy in identifying Alzheimer’s-associated EEG patterns and is highly suitable for implementation in clinical settings to aid early and consistent AZD diagnosis.</p>