Tikhonov regularization gained a wide popularity in the context of Statistical inverse problems. In this article, we investigate and analyze fractional Tikhonov regularization scheme, which is in fact one of many generalizations of Tikhonov regularization, in learning theory. We delve into the theoretical framework of the fractional scheme to prove the consistency of the algorithm, and the convergence analysis of the solution. Furthermore, optimal rate of convergence has been established under Hölder source condition. A discussion on the rate of convergence is pursued with well established schemes in literature. Numerical experiment carried out through the academic data corroborate our analysis.