[en] The denuclearization verification process involves the localization of nuclear materials in the area of nuclear inspection. Various methodologies based on detector measurement using CsI(Tl), NaI(Tl) scintillators and Geiger–Müller (GM) counters have been studied to localize a nuclear material, but they are not suitable for application to a wide outdoor range. The Korea Institute of Nuclear Nonproliferation and Control (KINAC) has developed a plastic scintillator-based small gamma-ray instrument (probe). In this study, artificial intelligence-based machine learning was applied to localize radioactive material based on probe measurement values. A localization algorithm model based on a Deep Neural Network (DNN) and Multiple Linear Regression (MLR) which are most used among various machine learning and deep learning algorithms was created. Then, the radioactive material was localized based on the measured value and compared with MCNP6-based simulation data. The performance of the DNN and MLR algorithms was evaluated through a coefficient of determination (R²) and Root Mean Square Error (RMSE). The results for the R² and RMSE of the DNN algorithms model are 0.9488 and 3.5734 m. The R² and RMSE of the MLR algorithm model are 0.8496 and 7.2452 m