Air pollution is a critical global environmental challenge, causing significant risks to public health, and ecosystems contributing to climate change. Extracting actionable insights from real-world pollution data is crucial for understanding and mitigating these risks. This chapter presents a detailed methodology for uncovering valuable patterns in air pollution data by applying the Knowledge Discovery in Databases (KDD) process. Using over five years of hourly \(PM_{2.5}\) data collected from air quality sensors across Japan, we show how to preprocess, transform, and analyze this data using a combination of Python libraries such as Pandas, Scikit-learn, and PAMI. We walk through data acquisition, pattern discovery, and visualization, emphasizing how spatial patterns of high pollution areas can facilitate location-specific policy decisions. The findings highlight the effectiveness of combining data science techniques with environmental data to address global challenges. This chapter provides a replicable framework for applying the KDD process in various large-scale datasets, demonstrating its relevance to environmental monitoring and public health research.