Heat Vulnerability Index (HVI) including heat exposure, sensitivity, and adaptive capability indicators were created for whole Australia.The dataset supports the development of a national heat vulnerability assessment toolkit for Australia, designed to identify areas and populations most susceptible to heat-related risks. The project addresses the growing need for understanding the relationship between urbanization, land surface temperature (LST), and the urban heat island effect, particularly for vulnerable communities. Integrating satellite-derived environmental data (LST, Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI)) with socioeconomic data, this toolkit provides a comprehensive resource for building heat-resilient cities and suburbs. This dataset includes both raw environmental data for the 2020-2021 Australian summer (November to March) and a calculated Heat Vulnerability Index (HVI) aggregated to the Australian Bureau of Statistics (ABS) Statistical Area Level 1 (SA1) polygon dataset. The HVI, based on the IPCC's vulnerability conceptual framework, is a composite index comprised of three core components: heat exposure (derived from LST), sensitivity to heat (influenced by socioeconomic factors), and adaptive capability. Each SA1 is assigned a vulnerability rating ranging from 0 to 5, with 0 indicating no population and 5 representing high vulnerability, based on the aggregated indicator scores and quartile distribution. The methodology employs Google Earth Engine (GEE) to derive LST, NDBI, and NDVI. The HVI, along with its components, allows for spatial analysis and facilitates understanding of the complex relationships between heat, environmental factors, and socioeconomic conditions, enabling targeted policy and decision-making at local levels. This work aims to support dynamic and interactive vulnerability assessment, enabling users to update and construct their own indicators and indices for diverse applications. Detailed methodology for HVI generation can be found in this paper. Additional resources are available on the project's GitHub repository, the web application, and the toolkit.