This paper presents a graph-based algorithm for identifying complex metabolic pathways in multi-genome scale metabolic data. These complex pathways are called branched pathways because they can arrive at a target compound through combinations of pathways that split compounds into smaller ones, work in parallel with many compounds, and join compounds into larger ones. While most previous work has focused on identifying linear metabolic pathways, branched metabolic pathways predominate in metabolic networks. Automatic identification of branched pathways has a number of important applications in areas that require deeper understanding of metabolism, such as metabolic engineering and drug target identification. Our algorithm utilizes explicit atom tracking to identify linear metabolic pathways and then merges them together into branched metabolic pathways. We provide results on two well-characterized metabolic pathways that demonstrate that this new merging approach can efficiently find biologically relevant branched metabolic pathways with complex structures.