In the present investigation, a novel neuro-swarming intelligence-based numerical computing solver is developed for solving second order nonlinear singular periodic (NSP) boundary value problems (BVPs), i.e., NSP-BVPs, using modeling strength of artificial neural networks (ANN) optimized with global search efficacy of particle swarm optimization (PSO) supported with the methodology of rapid local search by interior-point scheme (IPS), i.e., ANN-PSO-IPS. In order to check the proficiency, robustness and stability of the designed ANN-PSO-IPS, two numerical problems of the NSP-BVPs have been presented for different number of neurons. The outcomes of proposed ANN-PSO-IPS are compared with the available exact solutions to establish worth of the solver in terms of accuracy and convergence, which is further endorsed through results of statistical performance metrics based on multiple implementations.