The integrity of the global food supply is increasingly compromised by sophisticated fraudulent methods such as adulteration, mislabeling, and substitution. These deceptive practices introduce undisclosed and harmful sub¬ stances that often evade traditional detection, posing significant risks to public health and consumer trust. This review synthesizes cutting-edge advancements in analytical and digital technologies that are redefining food fraud surveillance. We explore the convergence of molecular diagnostics (e.g., DNA barcoding, clustered regu¬ larly interspaced short palindromic repeats (CRISPR), next-generation sequencing (NGS)), high-throughput spectroscopic platforms (e.g., Raman spectroscopy, mass spectrometry), biosensors, and digital innovations (e. g., artificial intelligence (AI), Internet of Things (IoT), blockchain, and lab-on-a-chip (LOC) devices). These in¬ tegrated approaches enable real-time, proactive detection of fraud across complex and globalized supply chains, offering sensitivity, scalability, and traceability previously unattainable. By framing food fraud as a form of emerging contamination, this work aligns with the paradigm shift toward predictive, data-driven risk assessment in contaminant detection. The review also addresses key translational bottlenecks such as cost, standardization, and interoperability, and proposes a systems-level roadmap to bridge scientific innovation with regulatory and industrial application. This interdisciplinary perspective advocates for a future where food authenticity assur¬ ance is intelligent, holistic, and resilient in the face of evolving adulteration threats.