This study focuses on improving the accuracy of satellite-based PM_2.5 retrieval, crucial for monitoring air quality and its impact on health. It employs machine learning to correct the AOD-to-PM_2.5 conversion ratio using various data sources. The approach produces high-resolution PM_2.5 estimates with improved accuracy. The method is flexible and can incorporate additional training data from different sources, making it a valuable tool for air quality monitoring and epidemiological studies.