Globally, mycotoxin contamination of food poses a severe hazard to public health and food safety. One of the most serious hazards to human health is the contamination of agricultural products with mycotoxins, which are toxic secondary metabolites generated by fungi. Chromatographic separation is a popular approach to detecting mycotoxins, often used in conjunction with mass spectrometry, which is an accurate method but requires specialized staff and a lengthy sample preparation process. Artificial intelligence (AI) is a highly precise and reliable technology for identifying mycotoxins in food. This unique method shows how multiple AI systems can be merged. Neural networks, machine learning approaches, and deep learning models were utilized to analyze complex datasets from various analytical platforms. Furthermore, we have emphasized the need for AI in conjunction with smart sensing technologies or other unconventional methods, such as spectroscopy, biosensors, and imaging methodologies, to identify mycotoxins more quickly and safely. Among other vital challenges in this area, we question the importance of employing large and diverse datasets to train AI models, debate the need to standardize the analytical approaches, and explore strategies for obtaining regulatory approval for AI-based procedures. Furthermore, this study provides some intriguing use cases and real-world business applications in which AI outperformed the more traditional methodologies in terms of its sensitivity and specificity and the time required by incorporating the most recent research findings and emphasizing the value of interdisciplinary collaboration among food scientists, engineers, and computer scientists for future paths in AI-enabled mycotoxin detection. Ultimately, AI has the potential to transform mycotoxin monitoring systems, enhancing food safety and public health globally.