How to deal with an adviser that wants to force you to get his desired results?
The meaning of unexpected results

It is important to be skeptical about your computational approach. However, at the same time, computational approaches are (almost) completely worthless if we just ignore them when results are unexpected (unless you already have dominant evidence that the result is not only unexpected but also simply wrong). An exception would be if your approach is in the area of generative models, where a parsimonious model is suggestive of an underlying mechanism, which is not the area of your model: you are trying to do prediction for an unknown case (extrapolation).

The art is in determining whether your initial model of the world (i.e., expectation) is wrong or if your computational model is wrong.

In a long discussion in chat, I think we came to a conclusion that in your specific case, it may be that this is an issue of extrapolation to a condition where you do not have truly comparable training data.

How to stop worrying and learn to love the model

If you want to convince your advisor, colleagues, peer reviewers, or yourself that your model should be trusted, your next steps are to test the conditions that lead to your result.

Do all the appropriate tests for model convergence in the original training. Check for input parameters that are outside the range in the training set. Use graphical representations of your model to show how input are mapping to outputs. Remove or scale variables to test the sensitivity of your model to those changes. And additionally, as your advisor suggests, figure out what it takes to make your model fit the expected result. All of these approaches will help you find if something is wrong in the model or support you if something is wrong in the prior expectations.