Asthma is a chronic respiratory condition affecting millions worldwide. Environmental factors, especially air pollution, are well-documented to exacerbate asthma symptoms, resulting in increased hospitalizations and fatality rates. Comprehending the correlation between asthma and air pollution is crucial for public health initiatives and policy formulation. Epidemiological studies have conventionally been employed to establish this link by gathering data from asthma patients, assessing air quality, and doing statistical analyses to identify correlations. Notwithstanding their utility, these studies frequently encounter limitations, including prolonged durations, difficulties in data collecting, and the incapacity to capture real-time correlations. Recently, machine learning techniques have attracted interest across other domains, including pollution monitoring. Supervised learning algorithms, specifically, have the capacity to reveal significant insights into the intricate link between asthma and air pollution in metropolitan environments. This may result in more focused and efficient public health initiatives. This project aims to create a precise and dependable forecasting model to guide public health plans and policies. This model will facilitate proactive decision-making, enabling healthcare professionals to allocate resources more efficiently and allowing policymakers to execute targeted actions to diminish air pollution and alleviate the effects of asthma on at-risk urban populations.