How to predict numerical value "y"? if I have datasets having different number of parameters
If you have to predict $y$ only given $a,b,c$, then as a first look just discard $d,e$ etc from other data sets*. Any kind of regression might be useful, you will have to explore that - you might as well start with linear regression as it is simplest. 

In general, if you are not sure about which algorithm to apply for best results, you should set up an experiment to try multiple models:


Decide on what "best" means by choosing a test metric (mean square error is a common choice for regression problems). 
Split your data into training, cross-validation and test sets.
Train different types of model on the training set:


Tune any hyper-parameters (of more complex models) by taking the best results from the cross-validation set. 
Pick your best cross-validation result as your guess at the best model. 

Finally get an unbiased estimate of its performance by using your test set. 




* There might be ways to use $d,e$ values to refine some models, but if you are at the stage of asking whether or not to use linear regression, this is not worth exploring initially.