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Foodborne illness remains a global concern, highlighting demand for rapid, sensitive, and accurate pathogen detection in heterogeneous food matrices. Conventional microbiological methods, though reliable, are often labour-intensive and slow, restricting prompt intervention. This review examines recent advances in artificial intelligence (AI)-powered biosensors for pathogen detection, focusing on their ability to overcome matrix-induced challenges. Using integrating advanced biorecognition elements with machine-learning algorithms, particularly convolutional neural networks (CNNs), this approach achieves colony-forming-unit (CFU) detection within 60 minutes, significantly faster than culture-based assays. CNN models enable automated feature extraction and pattern recognition, delivering classification accuracies of high across diverse food systems. New methods that combine AI with Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-CRISPR-associated protein (Cas) molecular diagnostics enable ultra-specific nucleic acid recognition and intelligent signal interpretation, allowing for cost-effective monitoring and real-time decision-making. Despite these developments, large-scale implementation is hampered by issues like inter-laboratory heterogeneity, data reliance, and enzyme thermal instability. Future directions include scalable commercialization routes for CRISPR-Cas biosensors, explainable AI for regulatory acceptability, and federated learning for safe multi-site data integration. Accelerating adoption requires cross-sector cooperation between researchers, business, and regulatory organizations like the European Food Safety Authority (EFSA) and the U.S. Food and Drug Administration (FDA). When taken as a whole, AI-powered biosensing is a promising step toward intelligent, reliable, and deployable technologies to protect the world's food supply. • AI-powered biosensors enable rapid pathogen detection in complex food matrices. • CNNs reduce matrix-induced noise and improve accuracy across diverse food systems. • CRISPR–Cas biosensors provide ultra-specific, amplification-free nucleic acid detection.