Prefetching is a process in which the necessary portion of data is predicted and loaded into memory beforehand. The increasing usage of geographic data in different types of applications has motivated the development of different prefetching techniques. Each prefetching technique serves a specific type of application, such as two-dimensional geographic information systems or three-dimensional visualization, and each one is crafted for the corresponding navigation patterns. However, as the boundary between these application types blurs, these techniques become insufficient for hybrid applications (such as digital moving maps), which embody various capabilities and navigation patterns. Therefore, a set of techniques should be used in combination to handle different prefetching requirements. In this study, a priority-based tile prefetching approach is proposed, which enables the ensemble usage of various techniques at the same time. The proposed approach manages these techniques dynamically through a fuzzy-logic-based inference engine to increase prefetching performance and to adapt to various exhibited behaviours. This engine performs adaptive decisions about the advantages of each technique according to their individual accuracy and activity level using fuzzy logic to determine how each prefetching technique performs. The results obtained from the experiments showed that up to a 25% increase in prefetching performance is achieved with the proposed ensemble usage over individual usage. A generic model for prefetching techniques was also developed and used to describe the given approach. Finally, a cross-platform software framework with four different prefetching techniques was developed to let other users utilize the proposed approach.