I tried some python optimization functions and some of them needed Jacobian matrix prior for faster convergence. I understand Jacobians are basically transformation matrices that data from one space to another or carrying gradients information. Can someone explain me with some literature references, how the speed up is achieved?
You haven't told us exactly what optimization routine you're using, so it's difficult to provide a very specific answer to your question.
However, if you don't supply your own Jacobian function then the optimization routine that you're using is presumably approximating the derivatives using a finite difference approximation scheme. Computing finite difference approximations to the derivative requires many function evaluations and this slows down the optimization process. Furthermore, the inaccuracy of such approximate derivatives can cause the algorithm to require more steps to converge to an optimal solution and thus run more slowly.
The advantages of using exact analytical derivatives rather than finite difference approximations are discussed in most textbooks on nonlinear programming. See for example Practical Optimization by Gill, Murray, and Wright.