@Scortchi thanks for the tip, I will ask a separate question.

@Scortchi, sorry for the confusion. Eg. in a power transformation log(y)= b0 + b1log(x), the back transformation of the predicted values are ŷ = 10^(b0 + b1log(x)). If I have one predictor that requires power transformation and another that requires a reciprocal model, in which case back transformation is ŷ = 1 / ( b0 + b1x ), how do I go about completing the back transformation of the predicted values? Does that make more sense?

Thanks, @Scortchi, on both accounts. I am trying to avoid a polynomial or spline-based regression because I am unable to interpret the models as clearly is if it were a standard linear regression with transformed predictors. I actually ran into this problem recently as I could not interpret the GAM I had used, which used thin-plate regression, outside of looking at the multiple R of the model. It also had poor predictive power despite having good descriptive power. If you have any insight into the follow up question I posed in a comment to Data Science Dojo, I would be very grateful.

Thank you, whuber. I have added a follow up question in the comment under Data Science Dojo's answer, if you would like to chime in there as well.

D.S.D., thank you very much for the detailed explanation. I will review whuber's website today. My follow up question is this, if you are willing: in order to back transform the predicted values to their original units of measure, do I take the inverse transformation of the coefficients in the multiple regression and then apply the model equation to the original IV's prior to their transformation? In addition, as I transform the predictors and back transform them, does the intercept require transformation? I would imagine so as the intercept would change based on the transformed variables.