This work focuses on the integration of ML in asthma management. It affects 7% of the population and it required careful monitoring of bio markers and its triggers in the environment. There are many ML techniques that are been used recently to predict and monitoring such illness. Many factors affect the monitoring though, such as environmental conditions, patient specific illness factors. This work highlights significant advancement in the field of ML in the predication of Asthma. The research highlights significant developments in three key areas: ML-based classification models for diagnosis, sensor-integrated inhaler devices for monitoring medication adherence, and adaptive eHealth systems for personalized asthma attack prediction.