Nano-AI synergy in food chemistry: smart analytical tools for quality, safety, and nutritional profiling

The convergence of nanotechnology and artificial intelligence (AI) is revolutionizing the landscape of food chemistry, quality control, and nutritional assessment. This review explores the synergistic potential of Nano-AI platforms in enhancing food safety, real-time monitoring, nutrient delivery, and predictive analytics. Nanomaterials-including metallic nanoparticles, lipid-based carriers, and nanoemulsions-enable precise detection, targeted delivery, and enhanced bioavailability of food components. At the same time, AI techniques like machine learning, deep learning and neural networks provides potent tools for pattern recognition, for prediction of shelf life and for discover contaminants from complex sensor data. These technologies, combined have enabled the emergence of smart biosensors, smart packaging and self-enacting quality control systems designed for Industry 4.0 settings. Twenty case studies illuminate the latest applications and breakthroughs, from AI-empowered SERS nanoprobes for pesticide detection and deep learning-based models for interpreting nano-biosensor outputs to personalized nutrition solutions driven by real-time nano-AI diagnostics. This review has been refined to emphasize Nano-AI technologies directly relevant to food chemistry, including nanosensor-assisted spectroscopy, AI-driven spectral deconvolution, and nanomaterial-enhanced food analysis, ensuring a focused and chemically grounded discussion. By bridging progress in material science, computational modelling, and food technology in a single collection, this paper provides a full-spectrum roadmap to researchers, food technologists, and regulators interested in achieving smart, responsive, and sustainable food systems. Finally, strategic insights are also proposed on how Nano-AI might revolutionise food safety, precision nutrition, and global food security in the upcoming decades. This review primarily focuses on literature published within the last 10 years (2014 - 2025), with an emphasis on studies from the past 5 years that reflect the rapid developments in Nano-AI technologies.