A massively multiplayer online game (MMOG) has hundreds of thousands of players who play in the game concurrently. The players consume a great deal of CPU, memory and network bandwidth resources in MMOGs. We combine MMOGs with cloud computing environments. We use virtual machine servers (VMSs) in cloud computing environments instead of traditional physical game servers. By using a multi-server architecture, we divide a game world into several zones, and each zone consists of at least a VMS to execute game processes and exchange game information among players in the zone. In addition, we design an adaptive neural fuzzy inference system (ANFIS) and also an artificial neural network (ANN) to predict the load of each zone and decide a resource allocation policy to be performed by the VMS. Experimental results show that the mean square error of the ANFIS-based load prediction is lower than that of the ANN-based load prediction. Therefore, we incorporate the ANFIS prediction method along with the five resource allocation policies to the MMOG cloud. In terms of average access time, the proposed ANFIS-based DLPSVMS resource allocation method is 16.7% better than the ANFIS-based DLP, where DLP is an existing deep-level partitioning (DLP) method. Furthermore, the proposed method has the smallest number of VMSs used among the three methods. The evaluation results show the feasibility of applying the proposed resource allocation method to MMOG clouds.