Is Max Pooling and Conv used on anything else but images?
As @Aditya mentioned, we can use 1D Convolutions and Max Pooling for text classification as well. It has been used in sentiment analysis and gives quite good performance too. See here and here.

Another useful application is in signal processing. Classifying data from sensors of all kinds is a task for CNNs.


  You can develop a Human Activity Recognizer using 1D Convolutions. See
  here.


But, why not use an RNN instead of a CNN?

RNNs require a higher level of data preprocessing and have low inference speed if you are running them on a smartphone ( or any other IoT device ). CNNs are pretty fast in this case.

Audio classification using MFCC is performed using 1D Convolutional NN. See here.


  2D Convolutions are mainly used in image concerned ML tasks. They could extract spatial features from the 2D arrays ( an image ). In some cases, you can use them on 2D data which is not an image.