Mycotoxins from molds and fungus, which grow well in warm, humid environments, and chemical contamination are the main causes of foodborne sickness, which affects both the official and unofficial food sectors nationally. The sensitivity, convenience of use, and simplicity of the enzyme-linked immunosorbent test (ELISA) make it a useful instrument for ensuring the safety of food. Its restricted sensitivity and accuracy, however, have prevented its wider employment in food analysis, necessitating the development of more potent remedies. Over 10,000 ELISA values from a dataset that included samples of both fresh and spoiled food were used to train the AI algorithm. The platform had a 98.5% accuracy rate in distinguishing between fresh and rotting samples, which was far better than standard methods, which typically only achieve 85% accuracy. In addition, the artificial intelligence (AI) powered approach reduced the detection time by 40%, allowing for quicker reaction times and real-time tracking. Significant progress has been made thus far; combining AI with ELISA can improve analysis speed and accuracy. Additionally, in a case study involving dairy products. The AI-ELISA system showed its ability to prevent foodborne infections by identifying spoiling signs up to 24 h earlier than normal industry standard. Nanomaterials-based ELISA, or nano-ELISA, has thankfully substantially enhanced ELISA and demonstrated exceptional performance due to its high amount, cheap charge, and easy details. This paper talks about recent advancements in nano-ELISA and how they may be used for food analysis and how AI improving the ELISA. These developments offer a fresh perspective and new strategies for other approaches.