Cognitive radio (CR) is the next-generation wireless communication system that allows unlicensed users (or secondary users, SUs) to exploit the underutilized spectrum (or white spaces) in licensed spectrum while minimizing interference to licensed users (or primary users, PUs). This article proposes a SpectruM-Aware clusteR-based rouTing (SMART) scheme that enables SUs to form clusters in a cognitive radio network (CRN) and enables each SU source node to search for a route to its destination node on the clustered network. An intrinsic characteristic of CRNs is the dynamicity of operating environment in which network conditions (i.e., PUs' activities) change as time goes by. Based on the network conditions, SMART enables SUs to adjust the number of common channels in a cluster through cluster merging and splitting, and searches for a route on the clustered network using an artificial intelligence approach called reinforcement learning. Simulation results show that SMART selects stable routes and significantly reduces interference to PUs, as well as routing overhead in terms of route discovery frequency, without significant degradation of throughput and end-to-end delay.