The fast-growing field of nanotheranostics is revolutionizing cancer treatment by allowing for precise diagnosis and targeted therapy at the cellular and molecular levels. These nanoscale platforms provide considerable benefits in oncology, including improved disease and therapy specificity, lower systemic toxicity, and real-time monitoring of therapeutic outcomes. However, nanoparticles' complicated interactions with biological systems, notably the immune system, present significant obstacles for clinical translation. While certain nanoparticles can elicit favorable anti-tumor immune responses, others cause immunotoxicity, including complement activation-related pseudoallergy (CARPA), cytokine storms, chronic inflammation, and organ damage. Traditional toxicity evaluation approaches are frequently time-consuming, expensive, and insufficient to capture these intricate nanoparticle-biological interactions. Artificial intelligence (AI) and machine learning (ML) have emerged as transformational solutions to these problems. This paper summarizes current achievements in nanotheranostics for cancer, delves into the causes of nanoparticle-induced immunotoxicity, and demonstrates how AI/ML may help anticipate and create safer nanoparticles. Integrating AI/ML with modern computational approaches allows for the detection of potentially dangerous nanoparticle qualities, guides the optimization of physicochemical features, and speeds up the development of immune-compatible nanotheranostics suited to individual patients. The combination of nanotechnology with AI/ML has the potential to completely realize the therapeutic promise of nanotheranostics while assuring patient safety in the age of precision medicine.