The rising global incidence of Multiple Sclerosis (MS), an autoimmune disorder that impacts the central nervous system, demands novel diagnostic approaches for early detection and intervention. Given the challenges of irreversible MS progression and the complexity of traditional diagnosis methods, this study introduces a hybrid Artificial Intelligence (AI) framework that enhances MS diagnosis accuracy using MRI scans. Our model integrates a multi-view ResNet architecture with novel attention mechanisms—View Space Attention Block (VSAB) and View Channel Attention Block (VCAB)—to extract detailed features from 2D brain images. Additionally, we developed the Quantum RIME (QRIME) algorithm, which combines RIME and Quantum Behaved Particle Swarm Optimization (QPSO) for efficient dimensionality reduction, optimizing both accuracy and computational efficiency. The model was rigorously evaluated using sixteen UCI benchmark datasets and a dedicated brain MRI dataset of 425 scans (262 MS patients and 163 healthy controls), achieving a notable accuracy of 98.29%, precision of 96.49%, specificity of 97.65%, and an F1-score of 97.85%. These results not only demonstrate our model's exceptional capability in identifying MS with high precision but also highlight its potential applicability in diagnosing other neurological disorders. By emphasizing the transformative potential of AI in medical diagnostics, our work underlines the significance of innovative AI applications in enhancing early detection, ultimately aiming to enhance patient outcomes in the neurodegenerative disease domain.