In the twenty first century, there have been various scientific discoveries which have helped in addressing some of the fundamental health issues. Specifically, the discovery of machines which are able to assess the internal conditions of individuals, has been a significant boost in the medical field. Multiple Sclerosis (MS) is a demyelinating disease in which the insulating covers of nerve cells in the brain and spinal cord are damaged. This damage disrupts the ability of the nervous system’s parts in various forms to transmit signals, therefore results a wide range of symptoms including physical, mental and psychiatric problems. In this regard, most of the researchers have focused on improving the classification of brain lesions, especially those from MS, which is a relatively difficult task, using different algorithms, but mainly in determining the edge. However, the absence of learning methods have caused complexity and difficulties for having accurate results in the previous works. Therefore, this need motivated us to use various tools of learning methods with a focus on Cellular Learning Automata (CLA) to achieve more accurate results in detection of MS lesions. Cellular Learning Automata (CLA) is a hybrid model of two, Learning Automata and Cellular Automata, which is a simple discrete system that can exhibit complex calculations and behavior through simple and local rules. In this study, we aim to propose a new combinational algorithm using Support Vector Machine (SVM) used for classification and cellular learning automata (CLA) to increase the accuracy of MS lesion detection. The objective is to create artificial models using support vector machines (SVM) to classify MS and normal brain MRI images, analyze the effectiveness of these models and their potential to use them in multiple sclerosis (MS) diagnosis. In order to develop such combination method, we start with simple learning methods such as k-means to find MS lesion. The research was carried out in four stages, respectively, the algorithms are as follows; a) Semi-automatic method and use of K-Means, b) Automatic MS Segmentation Approach Based on Cellular Learning Automata, c) MS Segmentation Approach based on SVM, CLA and K-Means, d) Accurate Simulated Database, 3D MRI to 2D Images, using value of Binary Pattern Classification for MS Detection. The algorithms consist of pre-processing parts, detecting MS-hemispheres, feature extraction, classification and post-processing. In the pre-processing section, the brightness intensity of the normalized images and the brain region are first extracted. Then, to reduce the computational volume, the lesions are diagnosed. The proposed approach can be considered as a supplementary or superior method for other methods such as Graph Cuts (GC), fuzzy c-means, mean-shift, k-Nearest Neighbor (KNN). We try to see the benefits of having a 3D database but to use 2D vectors only for better comparison and more accurate results. Support vector machines (SVM) can be a useful tool during the multiple sclerosis (MS) disease diagnosis process, however, to be able to make better assumptions, more tests are needed.