multivariate clustering, dimensionality reduction and data scalling for regression
Great question, I will try to answer the aspects related to dimensionality reduction mentioned above.
$Dimensionality\: Reduction:$ The number of dimensions which you want to keep after doing PCA is an experimental value you can experiment with the number of dimensions and check you results. Although you have mentioned all 40 features are independent i would still ask you to do a correlation analysis of the variables.

Pca removes these correlated features and gives you a set of features which amount to explain most of your data. One advantage of dimensionality reduction is in regression. Correlated features often are a cause of multicollinearity. Doing a dimensionality reduction helps us get rid of this problem. Also once we have a reduced set of features we can apply the cluster analysis. The reason is K-means calculates the l2- distance between data points. in very high dimensions the concept of euclidean distance becomes less useful because of the curse of dimensionality. (probably the reason of the problems with your elbow curve.)

Hence bringing down the number of features using pca and clustering later will give a better idea of the groupings of the data.
The problem which you mention about PCA can be solved in fact it is not an issue at all. You can do regression with the features obtained by the data and only take the coefficients of the reduced feature set.Some other techniques which you might want to look at are regularization in regression.
For example L1-regularization helps in feature selection and helps with correlated variables.