Air pollution remains a critical issue in many countries, particularly in developing regions like India. In November 2019, New Delhi experienced an alarming Air Quality Index (AQI) level of 900, far exceeding the ‘severe’ threshold. Accurate air pollution forecasting is essential for informed decision-making due to its direct impact on public health. Effective forecasting depends on selecting suitable methods and evaluation measures to maximize prediction accuracy. This study investigates the prediction of air quality levels in India, emphasizing techniques to enhance forecasting precision and identify areas for further model improvement. Our research evaluates various Neural Networks, deep learning, and machine learning algorithms for predicting AQI. The findings reveal that among the techniques explored, the Long Short-Term Memory (LSTM) model outperforms others, demonstrating very good accuracy in capturing complex patterns in AQI data. This contribution serves as a valuable benchmark for experts aiming to refine forecasting methods and provides a reference for emerging researchers in the field.