Computational power has been increasing tremendously in recent years, resulting in an increase in data size and complexity. Volume rendering is an important method for visualizing such data, as it provides insight over the entire data set. However, traditional volume rendering techniques are not sufficient to handle such data because they are too large to fit in the memory of a single computer. Using a distributed system to visualize massive data improves the performance. That said, while parallelization has its benefits, it also creates challenges. There are two main approaches for parallel volume rendering: image order and object order. While these methods have advantages, they fail to achieve load balance in some cases. With our work, we present a hybrid parallel volume rendering algorithm that guarantees good performance and maintains load balance, since at each step of our hybrid approach the execution time is bounded. We compare our algorithm with the object order approach and demonstrate that our algorithm achieves performance and data scalability in cases where object order fails.