Nowadays, many areas of computer graphics need to process a huge amount of data. In order to visualize the data in realtime time, it is necessary to solve two different problems. The first problem is the limited time available to perform rendering. The second one arises from the restricted capacity of storage high-speed memories, like RAM and texture memories. In order to solve the first problem, this work has used multi-resolution techniques. The multi-resolution representation allows the application to work with a constant amount of data during the rendering process. The second problem has been solved by a predictive management memory system based on the virtual memory model. This work proposes an architecture that allows any storage device to be incorporated in the system. Devices are organized sequentially. The heart of the system consists in allocating an area of memory for each device and managing this space optimally. The predictive system aims to load in advance. The data that will probably be used by the application in the near future. This work proposes a specific adaptative prediction algorithm for the visualization problem. This algorithm exploits the information about the camera parameter variations as well as the data transfer rate, in order to decide what should be loaded. The camera parameters are used to determine which data will possibly be used by the application. The transfer rate is used to decide which resolution level of the data should be loaded to the high- speed devices, in advance. The predictive memory management system has been tested for real-time visualization of satellite images and virtual panoramas.