There's not likely to be any acceptable value for any of the criteria: rmse, sse and r-squared. They are better interpreted and applied comparatively rather than absolutely.

We generally consider a good R-square value ranging,

0.6~1

in engineering purposes, but I am really confused with the other three parameters.

I don't think there is any acceptable value for Root Mean Square Error (RMSE) and Sum of Squares due to error (SSE) but for Adjusted R-square it depend on what software was used to obtain the value if its a MINITAB software the Adjusted R-square should be above 65% for the data to be usable for any statistical evaluations

Need to minimise RMSE value. Acceptable value for R2 is higher i.e. close to 1.

There is no fixed threshold limit for RMSE and R squared. It is always better to have RMSE as low as possible. If RMSE of train data < RMSE of test , then we overfit the model and underfit if the viceversa scenario happens

There is no fixed value for SSE, but always we are looking forward for the minimum value that reflect the learning rate, whenever the value is soo small, the learning rate and the learning quality is high.

In terms of RMSE, the lower the better. Its like setting an R2 for calibration of 0.999, RMSE values of 0.1 below is very satusfactory. For adjusted r-squared it depends on the criteria set for the mode or the test usually a value of 0.6 above is fine , while values 0.8 and above surely depics a very good model, specially when very near to 1.0.

RMSE value ≥0.5 reflects the poor ability of the model to accurately predict the data.