Functional link artificial neural networks (FLANNs) are sensitive to weights’ initialization and adopted learning algorithms. Using an efficient learning algorithm and randomly initialized weights leads to improve FLANNs efficiency and performance. The performance of the TLBO-FLANN model was proved in literature through simulation study and comparison studies involving GA-FLANN, PSO-FLANN and HSFLANN. The current chapter presents a MapReduce-based TLBO-FLANN model to predict angiographic disease status value. The experimentations were carried out on data sets to prove the performance of the MapReduce-based TLBO-FLANN model.