The present paper provides a generalized model of network, namely, Hybrid Layered Network (HLN). We proved that the sets of all homogeneous, heterogeneous and multi-layered networks are subsets of the set of all HLNs depicting the model's generalizability. The proposed HLN is more efficient in encoding different types of nodes and edges {when compared to representing the same information through heterogeneous or multilayered networks}. It is found experimentally that the HLN model when used with GNNs improve tasks such as link prediction. In addition, we present a novel parameterized algorithm (with complexity analysis) for generating synthetic HLNs. The networks generated from our proposed algorithm are more consistent in modelling the layer-wise degree distribution of a real-world Twitter network (represented as HLN) than those generated by existing models. Moreover, we also show that our algorithm is capable of generating various multilayer and homogeneous network. Further, we define different structural measures for HLN {namely multilayer neighborhood, degree centrality, closeness centrality and betweeness centrality}. Accordingly, we established the equivalency of the proposed structural measures of HLNs with that of homogeneous, heterogeneous, and multi-layered networks.