District cooling systems (DCS) can effectively satisfy the cooling demands of multiple buildings. However, since the peak cooling demands usually appear in working hours, DCS may further increase the load peak and off-peak difference in power systems. An ice storage system (ISS) can be combined with DCS to achieve peak-load shaving and reduce the operational cost of DCS. However, finding the optimal operational strategy for DCS with an ISS is challenging since the system involves many complex physical processes that are hard to describe explicitly. To address this issue, this paper proposes a learning-based approach to optimize the operation of the DCS and ISS. Based on historical operational data, the proposed approach first trains multiple neural networks to approximate the complex mappings from decision variables and environment parameters to critical variables. With these trained neural networks, the dynamics of the system can be explicitly described with no need for establishing physical models. Then, the optimization problem can be formulated by using these neural networks to replicate objectives and constraints. A projected gradient descent algorithm is further employed to solve the previous optimization problem. Simulation results based on an actual DCS show that the proposed learning-based method can achieve excellent energy efficiency while maintaining the quality of services.