From catalogs of available declassified underground nuclear explosions, we compiled a comprehensive seismic waveform and event catalog termed GTUNE (Georgia Tech Underground Nuclear Explosions). Nuclear blast seismic records are sourced from previously prepared published datasets and openly available waveforms from online sources. All seismic traces were assembled into a user‐friendly format compatible with most python‐based machine learning (ML) packages. The GTUNE dataset includes the raw seismogram time series, event coordinates and origin time, sampling rate, station metadata, channel, epicentral distance, and P‐wave arrival time from the origin dataset when available and otherwise identified using a tuned automated picker. This is the first openly available comprehensive global underground nuclear blast seismic dataset and consists of 28,123 vertical‐component waveforms from 774 nuclear test blasts between 1961 and 2017 recorded between 0 and 90 epicentral degrees. For stations where data are not directly included due to data‐sharing restrictions, the mechanisms to acquire and process these data are included. In this article, we describe various steps involved in data collection and quality control to ensure accurate labels, and present summary properties of the catalog and data set. The catalog was initially developed for applications with ML methods but can be used for a wide range of studies such as source physics, earth structure, and event detection methodological development.