Measures of graph entropy have been widely used in information theory, biology, chemistry, and sociology, among other disciplines. The entropy of a probability distribution may be thought of as both an indicator of information and an indicator of uncertainty, and it is a number used in information theory to gauge the complexity of a graph. Mathematical and medicinal chemistry, including drug design, have previously made extensive use of information-theoretic network complexity measurements. Dendrimers belong to the world of molecular chemistry for stepwise controlled synthesis and to the world of polymers for repeating structures from monomers. A topological index is a numerical metric that describes the topology of a graph. Consequently , a topological index calculated for a molecular network is a quantitative indicator of molecular topology. Due to the simplicity of production and the speed at which these computations may be performed, these descriptors have gained great prominence in recent years. Characterizing molecular structure using topological methods, including numerical graph invariants, is a current trend in mathematics and computational chemistry. Such conceptual descriptors have also launched a broad range of applications in QSAR/QSPR investigations and is ideal for innovative molecular design, drug development, and risk assessment of chemicals. In this paper, various entropy measures along with the distance and degree based topological indices of highly efficient iridium cored electrophosphorescent dendrimer has been evaluated. Entropy measure is a topological feature that may be used to assess the complexity of chemical compounds.