Graphs, comprising nodes, edges, and additional information known as attributed graphs, are essential for many applications which represents the complex data structures. Accessing these types of graphs is a significant challenge due to privacy concerns and the sensitivity of information. In addition, finding the attributed graphs with different node types and edge types is another challenge. Lack of attributed graph datasets with its heterogeneity factors hinders the powerful graph analysis algorithms across different fields. To address these challenges, we propose the SDG_HHAG Framework, a Synthetic Data Generation Framework comprising two approaches: 1) \(Synt\_H_{o}AG\) (Synthetic Homogeneous Attributed Graphs) to enrich existing graphs with node and edge attributes, and 2) \(Synt\_H_{e}AG\) (Synthetic Heterogeneous Attributed Graphs) to create and generate synthetic heterogeneous attributed graphs. We conducted experiments using the proposed approaches, and results reveal that these approaches are flexible to generate the synthetic graphs with attributes which are useful for graph analytics. Further, generated synthetic datasets are used for applications of social networks such as community detection and for the applications of routing problem such as query reachability.