To obediently and modestly observes the asthma patient's atmosphere to classify the presence of two asthma-exacerbating activities, smoking and cooking using the Foobot sensor. Asthma management is challenging as it involves understanding causes and avoiding triggers that are both multi-factorial and distinctive to every individual. Moreover, it is tricky for doctors to constantly monitor the health of lots of patients and the environmental triggers simultaneously; or to get sufficient data on the environment in which the patient lives. A data-driven approach to develop a continuous monitoring-activity detection system aimed at understanding and improving indoor air quality in asthma management. In this learning, we were productively talented to notice a high absorption of Particulate Matter (PM), Volatile Organic Compounds (VOC), and Carbon Dioxide (CO2) during cooking and smoking activities. We detected (a) smoking with an error rate of 1%, (b) cooking with an error rate of 11%, and (c) obtain an overall 95.7% percent accuracy classification across all events (control, cooking and smoking). A scheme will permit doctors and clinicians to associate potential asthma symptoms and exacerbation information from patients with environmental factors without having to personally be present.