<p>Food safety is essential, but conventional detection techniques are slow and ineffective. New technologies such as artificial intelligence (AI), nanotechnology, and biosensors allow fast, accurate, real-time contaminant identification. AI-based machine learning improves food analysis, enhances imaging methods, and combines IoT and blockchain for tracking. Nanotechnology allows ultra-sensitive detection with nanoparticle-based sensors and nanozymes. Biosensors provide specific detection of toxins, pathogens, and contaminants using enzyme-based, immunosensor, and DNA/aptamer-based technologies. Next-generation electrochemical, optical, and paper biosensors increase practical usage. Hybrid concepts such as AI-sensing nanosensors and IoT-based smart packaging have intelligent, automated responses. Challenges of scaling up, regulation, affordability, and sustainability are limitations to widespread adoption. They can be overcome with collaboration, policy structures, and public sensitization. This review aims to explore the latest advancements in food safety technologies, focusing on AI-powered, nano-enabled, and biosensor-based strategies for rapid contaminant detection. It examines how artificial intelligence, nanotechnology, and biosensors transform food safety by enabling the real-time, sensitive, and accurate detection of microbial pathogens, chemical residues, allergens, and other contaminants. Additionally, it analyses recent innovations and effectiveness across different food matrices while addressing challenges such as regulatory concerns, integration barriers, and technological limitations. By providing a comprehensive evaluation of these emerging approaches, this review highlights their potential to enhance food safety monitoring, ensuring improved consumer protection and regulatory compliance.</p>