We report on the application of machine learning techniques to enhance the spatial resolution and reproducibility of quantum spin imaging using \(\beta \) -NMR and \(\mu \) SR methods. Our innovative imaging device, Mr. \(\beta \) RIGHT, visualizes material distribution based on interactions between polarized quantum spins and matter. We demonstrate that machine learning approaches, including linear regression, neural networks, and deep generative models (cVAE and cGAN), improve image reconstruction compared to conventional mathematical analysis. Using Geant4 simulations with up to 200,000 training events, we achieved spatial resolutions better than 1 mm. Notably, deep generative models enable interpretable reconstruction of spatial density patterns even under limited statistics (as few as 100 events), providing a complementary approach to conventional deterministic analysis. These advances open new pathways for dynamic spin imaging and real-time material characterization.
