Aircraft noise and pollutant emissions are an important part of the sources of pollution around airport that directly or indirectly will affect harmful to human health and ecosystems. The effects of aircraft noise and emissions on the populations around airport are deal with annoying and sometimes dangerous. In order to address this issue, the research propose an integrating model of aircraft noise and pollutant emissions by combining effects of both noise and pollutant emissions using Artificial Neural Network (ANN) to determine the healthy risk level around the airport. Measurement of aircraft noise and pollutant emissions at Soekarno Hatta International Airport – Cengkareng Indonesia applied to validate the developed model as input data. The results have been obtained in this research, the best ANN architecture model is 4 variable input (CO, NOx, SOx, Noise Level), 10 for variable process (hidden layer) and one for output variable (healthy risk level) or it can be written 4-10-1 model. Based on ANN validation criteria, the best validation performance achieved at epoch 28 from 34 epochs with the Mean Squared Error (MSE) is 0.0093521. The correlation between targets and outputs are validated with R = 0.95756, which means that there is a close relationship between targets and outputs. The network output errors value approaches to zero, ranging from -0.4 to 0.6.