The tumor microenvironment (TME) is an environment that affects the growth, progression, and resistance of the tumor. AI is already transformational in the process of comprehending and attacking the TME. Using datasets with many more variables and different aspects, AI models can predict tumor behavior with greater precision than conventional techniques. In this work, the polyethylene glycol-poly (lactic-co-glycolic acid) (PEG-PLGA) nanoparticles coated with magnesium fluoride (MgF₂) and loaded with L-arginine have been developed to modulate the immunity in tumors using nitric oxide (NO). Designed to be released near the TME, PLGA-MgF₂ regulates the release of NO, producing an improvement in immune responses against cancer cells. Plasma-polymer coating of PLGA-MgF₂ nanoparticles showed an average size of 150 nm with MgF₂ shell thicknesses of 12–14 nm and L-arginine loads of 72–75%. The concentrations of NO did not change under the acidic tumor-like conditions, and the concentration was between 25 and 30 µM in the first 48 hours. Transmission electron microscopy (TEM) showed 75–120 particles per square micrometer in the tumor, and the immune cell infiltration increased by 35–45%, and the hypoxia in the tumor decreased by 30–36%. The deep neural networks and support vector machines reached 85–95% accuracy in tumor response classification using AI. The therapy led to 60% augmentation of T-cell activation, 85% tumor growth retardation, 4-fold elevation of the M1/M2 ratio, and 45% longer survival.