Multiple sclerosis (MS) is a complex autoimmune disease-causing damage to nerve coverings and disrupting communication between the brain and the body. Timely and accurate detection of individual responses to treatment is crucial for effective personalized MS therapy. Traditional models like logistic regression and decision trees offer some predictive accuracy, yet the variability in symptoms and treatment responses poses challenges. This research study suggests an innovative MS diagnosis approach, integrating machine learning (ML) techniques, specifically ensemble learning methods (random forest, bagging, stacking), and convolutional neural networks (CNNs). The complexity of MS and diverse patient responses necessitate sophisticated techniques. Ensemble learning enhances predictive capacity by capturing intricate relationships within heterogeneous data, including magnetic resonance imaging (MRI) and clinical records. CNNs, specialized in image analysis, contribute to spatial feature extraction from MRI scans, improving sensitivity to subtle MS-related changes.