Air pollution in urban environments has risen steadily in the last several decades. Such cities as Beijing and Delhi have experienced rises to dangerous levels for citizens. As a growing and urgent public health concern, cities and environmental agencies have been exploring methods to forecast future air pollution, hoping to enact policies and provide incentives and services to benefit their citizenry. With greater computing power in the twenty-first century, using machine learning methods for forecasting air pollution has become more popular. This project investigates the use of the LSTM recurrent neural network (RNN) as a framework for forecasting in the future, based on time series data of pollution and meteorological information in Beijing. Our results show that the LSTM framework produces equivalent accuracy when predicting future time stamps compared to the baseline support vector regression for a single time stamp. Using our LSTM framework, we can now extend the prediction from a single time stamp out to 5 to 10 hours in the future. This is promising in the quest for forecasting urban air quality and leveraging that insight to enact beneficial policy.