Distributed cloud computing, when integrated with smartphone capabilities, contribute to building an efficient multimedia e-health application for mobile devices. Unfortunately, mobile devices alone do not possess the ability to run complex machine learning algorithms, which require large amounts of graphic processing and computational power. Therefore, offloading the computationally intensive part to the cloud, reduces the overhead on the mobile device. In this thesis, we introduce two such distributed cloud computing models, which implement machine learning algorithms in the cloud in parallel, thereby achieving higher accuracy. The first model is based on MapReduce SVM, wherein, through the use of Hadoop, the system distributes the data and processes it across resizable Amazon EC2 instances. Hadoop uses a distributed processing architecture called MapReduce, in which a task is mapped to a set of servers for processing and the results are then reduced back to a single set. In the second method, we implement cloud virtualization, wherein we are able to run our mobile application in the cloud using an Android x86 image. We describe a cloud-based virtualization mechanism for multimedia-assisted mobile food recognition, which allow users to control their virtual smartphone operations through a dedicated client application installed on their smartphone. The application continues to be processed on the virtual mobile image even if the user is disconnected for some reason. Using these two distributed cloud computing models, we were able to achieve higher accuracy and reduced timings for the overall execution of machine learning algorithms and calorie measurement methodologies, when implemented on the mobile device.