Asthma is a rising significant global public health burden especially in the developing countries. The annual prevalence of severe asthma episodes is estimated from 1% to 21% for adults and over 20% for children aged 6每7 years. The prevalence of asthma varies widely around the world, ranging from 0.2% to 21.0% in adults and from 2.8% to 37.6% in 6- to 7-year-old children. The International Study of Asthma and Allergies in Children (ISAAC) reports a significant increase in the global prevalence of asthmatic episodes among children. In Namibia a prevalence of 11.2 % has been reported in adult populations. This increase in reporting of asthmatic episodes possibly reflects a greater awareness of Asthma. Similarly, the increase asthmatic episodes, morbidity and mortality among populations in Africa, Latin America and parts of Asia is a rising public health concern. Asthma is a chronic inflammatory disease of the conducting airways in which many cells of the innate and adaptive immune systems act together with epithelial cells to cause bronchial hyper-reactivity (BHR) (the tendency of smooth muscle cells in people with asthma to react to various stimuli. Nevertheless, the development of new asthma phenotyping and treatment including machine learning and big data have markedly improved treatment outcomes. In particular, the reclassification of severity of asthma as well as novel treatment options based on immunological biomarkers has improved outcomes among patients with severe asthma in developed countries. However, the integration of immunological interventions a mentioned above in asthma standard international treatment guidelines of developing countries remains limited or unknown. This may compromise the quality of asthma care received by this population and lead to sub-optimal outcomes particularly among patients with severe asthma in both children and adults. Understanding heterogeneity in severe asthma at the molecular level and identifying biomarkers characterizing subgroups are essential to developing new, targeted therapies and to selecting patients most likely to respond to these therapies. Aims and Scope: To describe he magnitude of Childhood Asthma Outline the role of biomarkers in the management of Childhood Asthma Describe the contribution of Machine learning and big data to asthma phenotyping in children Outline the management of severe asthma in children