Ischemic stroke is the most commonly occurring type of stroke and one of the most communal causes for disability and death in the world as per World Health Organization. Multiple factors such as hypertension, diabetes, arterial fibrillation, heart diseases, transient ischemic strokes, etc. contribute to ischemic stroke susceptibility. There is a compelling need for follow up checkups and post analysis to prevent further strokes. Apart from clinical tests, a lot of research is being carried out on computer based automated techniques and mechanisms for estimation of ischemic stroke risk. Ultrasound images of the carotid artery are used for development of noninvasive image based methods for stroke risk estimation however; carotid artery morphology, noise and artifacts in the ultrasound images can lead to false classification. Carotid intima media thickness is an indicator of future ischemic stroke. In this research, we have proposed an automatic ischemic stroke risk estimation approach using carotid intima media thickness from longitudinal carotid B-mode ultrasound images. Based on carotid intima media thickness, a classification scheme is proposed to associate the carotid artery stenosis with ischemic stroke risk. The proposed approach is tested and clinically validated on a data set of 100 longitudinal ultrasound images of the carotid artery. There is no significant difference between intima media thickness measurements obtained using our approach and the manual measurements by experts. The intra-observer error of 0.088, a Coefficient of Variation of 12.99%, Bland-Altman plots with small differences between experts (0.01 and 0.03 for Expert 1 and Expert 2, respectively) and Figure of Merit of 98.5% are obtained. The proposed approach makes the risk estimation process automatic and yet reduces the risk of subjectivity and operator variability for intima media thickness measurement. Additionally, some of stroke cases are suspected to be genetic as the patients do not suffer from the conventional risk factors. Extensive research has been conducted to investigate the unknown factors other than the conventional ones and their relationship with genetics. We have analyzed genotype data for stroke risk estimation. Nine classification models are used on the SNPs data to analyze and classify individuals. An accuracy of 88.16% is achieved by the proposed approach. Ischemic stroke risk has been correlated with genetic distances. For this purpose phylogenetic trees have been used. Analysis suggests that given two populations might be genetically close but they might be far with respect to ischemic stroke risk. Proposed research has addressed both the medical image analysis and genetic data analysis for stroke risk estimation. The proposed approach has achieved higher accuracy, specificity and sensitivity values when compared to existing approaches.