Asthma is a chronic lung disease that inflames and narrows the airways. Asthma is a multifactorial chronic illness, there is no uniformity of triggers factors of asthma attack on asthma patients with different degree of asthma severity. The better understanding of the trigger factors could lead a better asthma management for asthma patient. in this paper, we proposed a design of personalized asthma management system. in this system we used pattern sequential mining, clustering and classification to predicting the patient's asthma attack based on extracted rules from data mining. the main methodology is to identify the most important trigger factors based on the clustering and classification rules to be extracted. The contribution to this research area is to analyze the suitable data mining technique and algorithm for the proposed system.