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This paper investigates the idea of introducing learning algorithms into parking guidance and information systems that employ a central server, in order to provide estimated optimal parking searching strategies to travelers. The parking searching process on a network with uncertain parking availability can naturally be modeled as a Markov Decision Process (MDP). Such an MDP with full information can easily be solved by dynamic programming approaches. However, the probabilities of finding parking are difficult to define and calculate, even with accurate occupancy data. Learning algorithms are suitable for addressing this issue. The central server collects data from numerous travelers’ parking search experiences in the same area within a time window, computes approximated optimal parking searching strategy using a learning algorithm, and distributes the strategy to travelers. We propose an algorithm based on Q-learning, where the topology of the underlying transportation network is incorporated. This modification allows us to reduce the size of the problem dramatically, and thus the amount of data required to learn the optimal strategy. Numerical experiments conducted on a toy network show that the proposed learning algorithm outperforms the nearest-node greedy search strategy and the original Q-learning algorithm. Sensitivity analysis regarding the desired amount of training data is also performed.