Abstract Background: Migraine is a predominant headache that features pulsating pain that is mostly unilaterally provoked by regular physical activity and is accompaniedby nausea and/or photophobia and phonophobia. A range of autonomic, cognitive, and emotional disturbances may accompany mi-graine. High prevalence and elevated socioeconomic and personal effect have been recorded by epidemiological studies. Migraine has an incidence of 14% in the overall population for one year. The Global Burden of Disease Survey ranked it as the sixth largest worldwide cause of disability. Aim of Study: The aim of this study is to assess the role of volumetric software tools using artificial intelligence in evaluation of regional gray and white matter volume changes in patients having migraine without aura. Patients and Methods: We enrolled in this prospective study 20 patients, age ranged from 13 to 52 years old (mean =33.65 years, SD=10.68), 1 male and 19 female patients (5% and 95% respectively). Ten patients (50%) suffering from right side migraine, 8 (40%) from left side and two patients (10%) suffering bilaterally. 3D T1-weighted MRI images with dedicated parameters were uploaded to NeuroQuant software which is FDA approved software for segmental brain volu-metric studies. Automated reports for volume changes were obtained. Results: Focal hypertrophy was observed in 14 (70%) patients for cerebellar white matter, 11 (55%) patients for cingulate gyrus, 10 (50%) patients for nucleus accumabens, 10 (50%) patients for Hippocampus, 8 (40%) patients for thalamus, 8 (40%) patients for isthmus cingulate, 7 (35%) patients for posterior superior temporal sulcus, anterior cin-gulate, para hippocampaleach & 6 (30%) patients for cerebral white matter and ventral diencephalon. Less percentage was noted for focal atrophy, 9 (45%) patients for medial orbitof-rontal gyrus, 7 (35%) patients for posterior superior temporal sulcus & 4 (20%) patients for ventricles. Conclusion: Patients having migraine without aura show gray and white matter volume changes in different cerebral and cerebellar areas that could be detected by MRI and improved by using artificial intelligence software programs for accurate, fast, automated volume changes detection.