In this paper, we proposed a 'Projection Based Learning for Meta-cognitive Radial Basis Function Network (PBL-McRBFN)' classifier for effective diagnosis of Parkinson's disease. McRBFN is inspired by human meta-cognitive learning principles. McRBFN uses the estimated class label, the maximum hinge error and class-wise significance to address the self-regulating principles of what-to-learn, when-to-learn and how-to-learn in a meta-cognitive framework. Initially, McRBFN begins with zero hidden neurons and adds required number of neurons to approximate the decision surface. When a neuron is added, network parameters are initialized based on the sample overlapping conditions. The output weights are updated using a PBL algorithm such that the network finds the minimum point of an energy function defined by the hinge-loss error. The experimental results on parkinson's data sets based on vocal and gait features clearly highlight the superior performance of PBL-McRBFN classifier over results reported in the literature for detection of individual with or without PD.