Predictive Artificial Intelligence Models for Mycotoxin Surveillance and Mitigation in Poultry Production Systems

Mycotoxins are widespread contaminants in animal feeds and continue to pose serious threats to animal welfare, production efficiency, food security, and sustainable economic growth worldwide. Chickens are highly susceptible to multiple mycotoxins, such as aflatoxins (AFLs), deoxynivalenol (DON), fumonisins (FBs), zearalenone (ZEA), ochratoxin A (OTA), and T-2 toxin. Exposure to these toxins can damage intestinal integrity, compromise immune functions, impair nutrient absorption, and ultimately reduce production efficiency even at subclinical concentrations. Conventional mycotoxin detection methods, such as enzyme-linked immunosorbent assays (ELISA) and chromatographic techniques, provide accurate quantification but remain expensive, labor-intensive, time-consuming, and inefficient for large-scale screening, particularly when masked mycotoxins are present. The frequent co-occurrence of multiple mycotoxins further complicates risk assessment and effective management. Emerging analytical technologies, including hyperspectral imaging, biosensors, Internet of Things (IoT)-based platforms, and machine-learning algorithms, offer promising advancements for rapid detection and predictive risk forecasting. This review summarizes the toxicological impacts of major mycotoxins on poultry, outlines critical challenges in detection and prevention, and evaluates current artificial intelligence (AI) and machine learning (ML) approaches for mycotoxin identification, prediction, and management. A systematic literature search conducted across PubMed, ScienceDirect and Google Scholar identified 176 unique studies published between 2010 and 2026. Key knowledge gaps include limited availability of high-quality datasets and source code, inconsistent model interpretability, and poor reproducibility across production environments. By integrating conventional toxicology with data-driven approaches, this review highlights how predictive modeling can strengthen mycotoxin surveillance and support proactive mitigation strategies in modern poultry production systems.