Using the predictions of a model to train a new model
Yes, you can do that and it's absolutely fine and this strategy is often used as well. There are just a couple of things that you need to take care of while doing so:

Create good stratified testing data from the labeled 18000 images. If possible use k-fold splits.
Now predict the other unlabelled images and get the pseudo labels.
Train a model on the whole dataset.
This is the most important step. Here you will validate your model on the k-fold splits and then evaluate if the performance of your model increases or not in comparison to the k-fold results on just the labeled data.

Keep iterating these steps till your score improves but always be careful that there is no data leak regarding the k-fold data.
I am trying to train a model. I only have around 8k labelled data and around 20k unlabelled data. What I was thinking is, is it OK to train and test a model using the 8k labelled data and use that model to predict the classes of the remaining 20k unlabelled data. Then, using all the data creating a new model. I am trying to do binary classification and the model works quite well on the initial labelled population.