This chapter delves into the significance of data identification in air quality monitoring, focusing on techniques that can enhance the accuracy of data analysis and forecasting. With the rise of complex air quality datasets encompassing various pollutants and meteorological factors, effective data identification has become essential to filter out noise and extract meaningful patterns. The chapter reviews two primary methods: feature selection and feature extraction. Feature selection emphasizes identifying the most impactful variables, while feature extraction transforms raw data to capture key trends better. A comparative analysis of feature selection methods—filter and wrapper—demonstrates the superior predictive accuracy of the wrapper method, particularly in multistep forecasting. Additionally, performance evaluations of statistical feature extraction and time-frequency analysis (via DWT) reveal the unique advantages of each approach for different prediction scenarios, thereby underscoring the importance of tailored data identification strategies for optimal air quality forecasting.