Should I make single machine learning model for predicting price of house or 6 different models, given 6 datasets for different cities?
Both options would work. It would depend on which method you want to use, what you want to do afterwards, or computing capacity.
E.g. developing a model for each city might allow you to contrast and compare the outcomes more simply. But you can also do multivariate predictions to predict for each city even once you have combined your dataset.
Also if your dataset is very large it might make sense to treat the cities independently to make the computations slightly cheaper.