L2 regularization also called weight decay is one of the regularization methods like (L1 or Dropout) as you have also mentioned ‘avoiding overfitting’
Learning rate decay aims to update learning rate hyperparameter considering time. Because choosing the learning rate is challenging as a value too small may result in a long training process that could get stuck, whereas a value too large may result in learning a sub-optimal set of weights too fast or an unstable training process. We want to take ‘bigger’ steps when we started training but we want to be careful while closing to optimum. That is we don’t want to overshoot to optimum.
Regarding your question, yes you can both of them like L2 to prevent overfitting, learning rate decay not to overshoot.
I hope it is clear a little bit.