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.

Metal oxide-based nanohybrids have become multipurpose materials that connect basic nanoscience with useful technology uses. They are appealing for a variety of sectors, from biology to energy and environmental remediation, due to their tunable physicochemical features and synergistic interactions. The main synthesis approaches—physical, chemical, and green/biological—are presented in a cohesive manner in this review, emphasizing their benefits, drawbacks, scalability, and appropriateness for various application requirements. Characterization methods including spectroscopy, diffraction, and microscopy are presented as crucial connections that link final functional performance with structure, composition, and morphology in addition to being analytical instruments. Additionally, the review incorporates new advancements such as data-driven intelligent material design, sustainable synthesis utilizing microbes and plant extracts, and machine learning-assisted process optimization. All things considered, this work provides a coherent overview linking synthesis techniques, property assessment, and application potential, providing insights that can direct the future development of effective, environmentally friendly metal oxide nanohybrids designed for practical technological deployment.