Estimating the accuracy of a model? Given that I have a machine learning model.

I evaluated the model over several labeled datasets and acquired the accuracy (or any other metrics) for these datasets.

Now I receive a new dataset without labels. I run the model and got the prediction.

Is there any way to estimate the accuracy of my model on the new dataset?

I was thinking of measuring the similarity of the datasets (in the feature space), then based on the similarity to predict the accuracy of the new dataset. For instance, if the new dataset is not far from the old dataset, the accuracies might be similar.
Is there any way to estimate the accuracy of my model on the new dataset?


No.

If you want to evaluate the classification accuracy of your model, you need to know the ground truth to compare, simple as that. Estimating the similarity of the datasets is totally irrelevant, unless there is prior knowledge that feature similarity  leads to label similarity.