Asthma, a chronic respiratory disease affecting millions of people worldwide, can vary in severity depending on individual triggers such as Carbon Dioxide, Particulate Matter, dust mites, tobacco smoke, and indoor household activities such as cooking, cleaning, use of heating, and window opening, which can have a negative impact on indoor air quality (IAQ) and exacerbate asthma symptoms. Investigating the relationship between IAQ and asthma severity, a case study was conducted on five asthmatic participants from Bradford, UK. IAQ was measured using IoT indoor air quality monitoring devices. Indoor activities were recorded using a daily household activities questionnaire, and asthma severity was assessed using the Asthma Control Questionnaire (ACQ). Machine learning prediction models were used to analyse various IAQ parameters, such as particulate matter, carbon dioxide, and humidity levels, to identify the most significant predictors of asthma severity with IAQ. The study aimed to develop targeted interventions to improve IAQ and reduce the burden of asthma. Results showed that higher asthma severity scores were associated with increased indoor activity and higher levels of indoor air pollution. Some interventions were implemented to improve ventilation hours, significantly improving IAQ and reducing asthma symptoms, particularly those with more severe asthma. The findings indicate that interventions targeting IAQ, and indoor activities can effectively reduce asthma severity, with up to a 60% reduction in symptoms for asthma patients.