The radial basis function and multilayer perceptron architectures diverge significantly as theoretical and practical approaches to neural networks. While each exhibits unique strengths in classification performance, the potential for integrating these tools remains a compelling, yet understudied, area of research. In this paper, we introduce two different approaches for the integrated architecture. The first approach applies the mixture of MLP dense layers and RBF layers. The proposed model applies an optimized kernel initialization mechanism to provide a fast convergence. In the second architecture model, a novel custom network layer architecture is presented which can balance between the MLP and RBF layer mode. Tested across various classification tasks, our proposed neural network model consistently demonstrated superior performance.