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Ultra-intense laser and plasma interactions with their ability to accelerate particles reaching relativistic speed are exciting from a fundamental high-field physics perspective. Such relativistic laser-plasma interaction (RLPI) offers a plethora of critical applications for energy, space, and defense enterprise. At AFIT’s Extreme Light Laboratory (ELL), we have demonstrated such RLPI employing a table-top ∼10mJ, 40 fs laser pulses at a kHz repetition rate that produce different types of secondary radiations via target normal sheath acceleration (TNSA). With our recent demonstration of laser-driven fusion, the secondary radiations generated are neutrons, x-ray emission, and MeV energy electrons and protons—all at a kHz rate. To achieve the high repetition rate, we developed the enabling kHz-repetition-rate-compatible liquid targets in the form of microjets, droplets, and submicron-thick sheets. These targets, combined with high repetition rate diagnostics, enable a unique, real-time feedback loop between the experimental inputs (laser and target parameters) and generated sources (x-rays, electrons, ions, etc.) to develop machine learning (ML)-based control of mixed radiation. The goal of this paper is to provide an overview of the capabilities of ELL, describe the diagnostics and characteristics of the secondary radiation, data analysis, and quasi-real-time ML functionality of this platform that have been developed over the last decade and a half.