In embedded computer systems there are often tasks, implemented as stand-alone devices, that are both application-specific and compute intensive. A recurring problem in this area is to design these application-specific embedded systems as close to the power and efficiency envelope as possible. Work has been done on optimizing singlecore systems and memory organisation, but current methods for achieving system design goals are proving limited as the system capabilities and system size increase in the multi- and many-core era. To address this problem, this thesis investigates machine learning approaches to managing the design space presented in the interconnect design of embedded multi-core systems. The design space presented is large due to the system scale and level of interconnectivity, and also feature inter-dependant parameters, further complicating analysis. The results presented in this thesis demonstrate that machine learning approaches, particularly wkNN and random forest, work well in handling the complexity of the design space. The benefits of this approach are in automation, saving time and effort in the system design phase as well as energy and execution time in the finished system.