Air quality monitoring is critical because of its impact on urban and rural areas, where pollution from transportation, fuel use, and industrial activities threatens life quality. Effective monitoring models are essential, but traditional environmental stations are costly. Low-cost sensors offer an alternative, but they face challenges from external factors such as weather and human behavior. Machine learning (ML) techniques, including Multiple Linear Regression (MLR), Decision Trees, Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machines (SVM), are increasingly used for accurate air quality forecasting. This review summarizes recent ML methods and their applications for improving air pollution monitoring.