Food safety remains a critical global challenge with far-reaching implications for public health, economic stability, and social well-being. Contamination from chemical and microbiological hazards is a major cause of food losses, with an estimated 14% of food products lost before reaching consumers. This review examines the role of smart technologies particularly digital tools in addressing key challenges related to food safety. The focus is on innovations that enhance detection, monitoring, and traceability across the food supply chain. Integrating these advanced technologies enables early contaminant detection, accurate risk prediction, and improved management of food safety practices throughout production, distribution, and consumption. The adoption of smart technologies is essential to strengthening gl@@obal food safety systems, reducing foodborne illnesses, and ensuring safer, more reliable food for consumers worldwide. Graphical abstract

Health monitoring has become a major concern in current society especially due to the continuously rising incidences of chronic ailments. Some of the primary monitoring techniques face a severe disadvantage in that they are incapable of identifying even the subtlest of health changes in real-time. Other systems also have difficulties handling large scales, non-linear, dynamic data, for instance, from wearables’ sensors. To deal with such problems, this work developed a new approach based on Autoencoder for learning the features without requiring the label and GAN for the online detection of the anomalous data in real-time health information. It involves wearables with IoT for perpetually monitoring the patients and using sophisticated detection algorithms for feasible cloud analysis. The initial performance shows a better accuracy/precision, and recall compared to the conventional models and serves as an efficient means to provide timely basic health intelligence.