<p>Shannon’s entropy is a fundamental metric for assessing species diversity. It is widely used in ecology due to its capacity to capture the complexity of community structure without bias toward rare or abundant species. However, accurately estimating Shannon’s entropy becomes challenging when species inventories are incomplete, a common issue in ecological research. This study develops a novel estimator to improve the precision and reliability of Shannon’s entropy estimates under such conditions. In the simulation study, the proposed estimator was evaluated against eight established estimators using diverse surveyed ecological datasets as true populations, including Malaya butterflies, beetles, small mammals, corals, and insects, across varying sample sizes. For most scenarios, the results demonstrate that the proposed estimator consistently outperforms traditional methods, particularly in small and moderate sample scenarios, by delivering lower bias and more significant stability. This enhanced accuracy makes&#xa0;the new estimator a reliable tool for biodiversity assessment, with applications extending beyond ecology to fields such as medical and environmental science. The study provides a refined framework for biodiversity analysis, addressing critical challenges in ecological data collection and entropy estimation.</p>