This paper addresses the problem of delay-range-dependent iterative learning reliable guaranteed cost control of a class of batch processes with uncertainties, state delay and actuator failures, where the main contribution is in the relevant concept of H-infinity learning fault-tolerant guaranteed cost control based on an equivalent two-dimensional system description of these processes. The H-infinity iterative learning reliable guaranteed cost controller (ILRGCC) is formulated to include a robust extended feedback control for ensuring the performances over time and an iterative learning control (ILC) for improving the tracking performance from cycle to cycle, such that it can not only guarantee the closed-loop convergence along both the time and the cycle directions but also satisfy both the H-infinity performance index and a cost function preserving upper bounds for all admissible uncertainties and any actuator failures. To achieve the least guaranteed cost for the closed-loop system, a convex optimization problem with linear matrix inequality (LMI) constraints is formulated for the design of the optimal guaranteed cost controller such that the delay-range-dependent existing condition of the proposed ILRGCC design can also be given by LMIs, where the lower and upper delay bounds only change along time t direction. An illustrative example of applications to the nozzle packing pressure control in injection molding has been developed to demonstrate the effectiveness and merits of the proposed method.